diff --git a/Modules/ghquadm.ado b/Modules/ghquadm.ado
deleted file mode 100644
index c64290f..0000000
--- a/Modules/ghquadm.ado
+++ /dev/null
@@ -1,85 +0,0 @@
-program define ghquadm
-* stolen from gllamm6 who stole it from rfprobit (Bill Sribney)
-	version 4.0
-
-	parse "`*'", parse(" ")
-	local n = `1'
-	if `n' + 2 > _N  {
-		di in red  /*
-		*/ "`n' + 2 observations needed to compute quadrature points"
-		exit 2001
-	}
-
-	tempname x w xx ww a b
-	local i 1
-	local m = int((`n' + 1)/2)
-	matrix x = J(1,`m',0)
-	matrix w = x
-	while `i' <= `m' {
-		if `i' == 1 {
-			scalar `xx' = sqrt(2*`n'+1)-1.85575*(2*`n'+1)^(-1/6)
-		}
-		else if `i' == 2 { scalar `xx' = `xx'-1.14*`n'^0.426/`xx' }
-		else if `i' == 3 { scalar `xx' = 1.86*`xx'-0.86*x[1,1] }
-		else if `i' == 4 { scalar `xx' = 1.91*`xx'-0.91*x[1,2] }
-		else { 
-			local im2 = `i' -2
-			scalar `xx' = 2*`xx'-x[1,`im2']
-		}
-		hermite `n' `xx' `ww'
-		matrix x[1,`i'] = `xx'
-		matrix w[1,`i'] = `ww'
-		local i = `i' + 1
-	}
-	if mod(`n', 2) == 1 { matrix x[1,`m'] = 0}
-/* start in tails */
-	matrix `b' = (1,1)
-	matrix w = w#`b'
-	matrix w = w[1,1..`n']
-	matrix `b' = (1,-1)
-	matrix x = x#`b'
-	matrix x = x[1,1..`n']
-/* other alternative (left to right) */
-/*
-	above: matrix x = J(1,`n',0)
-	while ( `i'<=`n'){
-		matrix x[1, `i'] = -x[1, `n'+1-`i']
-		matrix w[1, `i'] = w[1, `n'+1-`i']
-		local i = `i' + 1
-	}
-*/
-	matrix `2' = x
-	matrix `3' = w
-end
-
-
-program define hermite  /* integer n, scalar x, scalar w */
-* stolen from gllamm6 who stole it from rfprobit (Bill Sribney)
-	version 4.0
-	local n "`1'"
-	local x "`2'"
-	local w "`3'"
-	local last = `n' + 2
-	tempname i p
-	matrix `p' = J(1,`last',0)
-	scalar `i' = 1
-	while `i' <= 10 {
-		matrix `p'[1,1]=0
-		matrix `p'[1,2] = _pi^(-0.25)
-		local k = 3
-		while `k'<=`last'{
-			matrix `p'[1,`k'] = `x'*sqrt(2/(`k'-2))*`p'[1,`k'-1] /*
-			*/	- sqrt((`k'-3)/(`k'-2))*`p'[1,`k'-2]
-			local k = `k' + 1
-		}
-		scalar `w' = sqrt(2*`n')*`p'[1,`last'-1]
-		scalar `x' = `x' - `p'[1,`last']/`w'
-		if abs(`p'[1,`last']/`w') < 3e-14 {
-			scalar `w' = 2/(`w'*`w')
-			exit
-		}
-		scalar `i' = `i' + 1
-	}
-	di in red "hermite did not converge"
-	exit 499
-end
diff --git a/Modules/rosali_custom/rosali_nobf.ado b/Modules/rosali_custom/rosali_nobf.ado
new file mode 100644
index 0000000..2eb8af2
--- /dev/null
+++ b/Modules/rosali_custom/rosali_nobf.ado
@@ -0,0 +1,1150 @@
+*! version 2.4 june2020
+*! Myriam Blanchin - Priscilla Brisson
+************************************************************************************************************
+* ROSALI: RespOnse-Shift ALgorithm at Item-level
+* Response-shift detection based on Rasch models family
+*
+* Version 1 : December 21, 2016 (Myriam Blanchin) /*rspcm122016*/
+* Version 1.1 : October 13, 2017 (Myriam Blanchin) /*option: MODA, automatic recoding of unused response categories*/
+* Version 2 : April, 2018 (Myriam Blanchin - Priscilla Brisson) /*option: GROUP, dichotomous group variable*/
+* Version 2.1 : October, 2018 (Myriam Blanchin - Priscilla Brisson) /* Version 1.1 + Version 2 */
+* Version 2.2 : February, 2019 (Priscilla Brisson) /* option nodif, optimization */
+* Version 2.3 : December, 2019 (Priscilla Brisson) /* option detail, + petites corrections */
+* Version 2.4 : June, 2020 (Myriam Blanchin) /* debug option detail + step C, modifs sorties et help */
+*
+* Myriam Blanchin, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* myriam.blanchin@univ-nantes.fr
+*
+* Priscilla Brisson, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* priscilla.brisson@univ-nantes.fr
+*
+* This program is free software; you can redistribute it and/or modify
+* it under the terms of the GNU General Public License as published by
+* the Free Software Foundation; either version 2 of the License, or
+* (at your option) any later version.
+*
+* This program is distributed in the hope that it will be useful,
+* but WITHOUT ANY WARRANTY; without even the implied warranty of
+* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+* GNU General Public License for more details.
+*
+* You should have received a copy of the GNU General Public License
+* along with this program; if not, write to the Free Software
+* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+************************************************************************************************************/
+
+program define rosali_nobf, rclass
+
+timer clear 1
+timer on 1
+
+syntax varlist(min=2 numeric) [if] [,GROUP(varlist) NODIF PRO DETail]
+
+preserve
+version 15
+tempfile saverspcm
+capture qui save `saverspcm',replace
+local save1=_rc
+
+if "`if'"!="" {
+    qui keep `if'
+}
+
+if "`pro'" != "" {
+	di "START"
+}
+
+/**************************************************************************/
+set more off
+set matsize 5000
+constraint drop _all
+
+local gp "`group'"
+
+tokenize `varlist'
+local nbitems:word count `varlist'
+
+	/* Vérif nb d'items pair */
+local mod=mod(`nbitems',2)
+if `mod'!=0 {
+  di as error "You must enter an even number of items : the first half of the items represents the items at time 1 and the second half the items at time 2"
+  error 198
+  exit
+}
+
+local nbitems=`nbitems'/2
+
+
+if "`group'"=="" & "`nodif'"!="" {
+	di as error "nodif can only be used with the group option ({hi:nodif} option). Please correct this option."
+	error 198
+	exit
+}
+
+local nbc: word count `group'
+if `nbc' >= 2 {
+	di as error "Only one variable can be used for group option ({hi:group} option). Please correct this option."
+	error 198
+	exit
+}
+
+	/* Vérif qu'il y a 2 groupes si l'option groupe est choisie */
+if "`group'"!="" {
+  qui tab `group'
+  local nbgrp = r(r)
+  if `nbgrp' != 2 {
+	di as error "Only 2 groups are possible for the group option ({hi:group} option). Please correct this option."
+	error 420
+	exit
+  }
+}
+/* recoder la variable de groupe en 0, 1*/
+
+if "`group'"!="" {
+	qui tab `gp', matrow(rep)
+	qui matrix list rep
+	if rep[1,1]+rep[2,1] != 1 & rep[1,1]*rep[2,1] != 0 {
+		forvalues i=1/`=rowsof(rep)'{
+			qui replace `gp'=`i'-1 if `gp'==rep[`i',1]
+			di "WARNING : `gp' `=rep[`i',1]' is now `gp' `=`i'-1' "
+		}
+	}
+	forvalues g = 0/1 {
+		qui tab `gp' if `gp' == `g'
+		local nbp_gp`g' = r(N)
+	}
+}
+
+
+
+/*item rename*/
+/*
+Items au temps 1 : 1 à nbitems ``j''
+Items au temps 2 : nbitems à 2*nbitems ``=`j'+`nbitems'''
+
+Si t varie, puis num item : ``=(`t'-1)*`nbitems'+`j'''
+*/
+
+
+local com_z = 0 // Indicatrice de recodage
+	/*verif modalités répondues*/
+if "`gp'" == "" {							// Si pas d'option groupe
+	forvalues j = 1 / `nbitems' {
+		local recoda_`j' = 0
+		qui tab ``j'', matrow(rect1_`j')				// Récupération des infos moda du temps 1
+		local minm`j'_t1 = rect1_`j'[1,1]
+		local maxm`j'_t1 = rect1_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''', matrow(rect2_`j')			// Récupération des infos moda du temps 2
+		local minm`j'_t2 = rect2_`j'[1,1]
+		local maxm`j'_t2 = rect2_`j'[r(r),1]
+
+		local minm_`j' = min(`minm`j'_t1',`minm`j'_t2')		// Info moda pour l'item j
+		local maxm_`j' = max(`maxm`j'_t1',`maxm`j'_t2')
+		local nbm_`j' = `=`maxm_`j''-`minm_`j'''
+
+		if `minm_`j'' != 0 & `com_z' == 0 {
+			local com_z = 1
+		}
+
+
+		//Recodage des réponses en 0, 1, 2, etc...
+		forvalues r = 0/`=`maxm_`j''-1'  {
+				qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
+				qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
+		}
+
+		// Vérif. Que toutes les modas sont utilisées & concordance entre temps
+		forvalues m = 0/`nbm_`j'' {
+			qui count if ``j'' == `m'
+			local nb_rn1 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m'
+			local nb_rn2 = r(N)
+			local nb_rn = min(`nb_rn1',`nb_rn2')
+
+			if `nb_rn' == 0 {		// Une moda n'est pas utilisée
+				local recoda_`j' = 1
+				if   `m' == 0 | `m' <= `minm`j'_t1' | `m' <= `minm`j'_t2' { // La moda 0 ou les moda min ne sont pas utilisées
+					local stop = 1
+					forvalues k = 1/`=`nbm_`j''-`m'' {
+						qui count if ``j'' == `=`m' + `k''
+						local v`k'1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k''
+						local v`k'2 = r(N)
+						if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+							qui replace ``j''= `=`m'+`k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged "
+							local stop = 0
+						}
+					}
+				}
+				else if `m' >= `maxm`j'_t1' | `m' >= `maxm`j'_t2' | `m' == `maxm_`j'' {	// La (ou les) moda max ne sont pas utilisée(s)
+					local stop = 1
+					forvalues k = 1/`m' {
+						qui count if ``j'' == `=`m' - `k''
+						local v`k'1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k''
+						local v`k'2 = r(N)
+						if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+							qui replace ``j''=`=`m' - `k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m' - `k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else {
+					if runiform()>0.5{											// Tirage au sort pour regrouper
+						local stop = 1
+						forvalues k = 1/`m' {
+							qui count if ``j'' == `=`m' - `k''
+							local v`k'1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k''
+							local v`k'2 = r(N)
+							if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+								qui replace ``j''= `=`m'-`k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems''' =`=`m'-`k'' if ``=`j'+`nbitems''' ==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+								local stop = 0
+							}
+						}
+					}
+					else {
+						local stop = 1
+						forvalues k = 1/`=`nbm_`j''-`m'' {
+							qui count if ``j'' == `=`m' + `k''
+							local v`k'1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k''
+							local v`k'2 = r(N)
+							if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+								qui replace ``j''=`=`m' + `k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+								local stop = 0
+							}
+							else {
+								if `stop' != 0 {
+									qui replace ``j''= `nbm_`j'' if ``j''==`m'
+									qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
+									di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
+									local stop = 0
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+else {									// Cas où l'option groupe est utilisée
+	forvalues j = 1 / `nbitems' {
+		local recoda_`j' = 0
+		qui tab ``j'' if `gp' == 0, matrow(rect1_g0_`j') matcell(nbrt1_g0_`j')					// Récupération des infos moda du temps 1pour chaque groupe
+		local minm`j'_t1_g0 = rect1_g0_`j'[1,1]
+		local maxm`j'_t1_g0 = rect1_g0_`j'[r(r),1]
+
+		qui tab ``j'' if `gp' == 1, matrow(rect1_g1_`j') matcell(nbrt1_g1_`j')
+		local minm`j'_t1_g1 = rect1_g1_`j'[1,1]
+		local maxm`j'_t1_g1 = rect1_g1_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''' if `gp' == 0, matrow(rect2_g0_`j') matcell(nbrt2_g0_`j')				// Récupération des infos moda du temps 2 pour chaque groupe
+		local minm`j'_t2_g0 = rect2_g0_`j'[1,1]
+		local maxm`j'_t2_g0 = rect2_g0_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''' if `gp' == 1	, matrow(rect2_g1_`j') matcell(nbrt2_g1_`j')
+		local minm`j'_t2_g1 = rect2_g0_`j'[1,1]
+		local maxm`j'_t2_g1 = rect2_g0_`j'[r(r),1]
+
+		local minm_`j' = min(`minm`j'_t1_g0',`minm`j'_t2_g0',`minm`j'_t1_g1',`minm`j'_t2_g1')		// Info moda pour l'item j
+		local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t2_g0',`maxm`j'_t1_g1',`maxm`j'_t2_g1')
+		local nbm_`j' = `=`maxm_`j''-`minm_`j''+1'
+
+		if `minm_`j'' != 0 & `com_z' == 0 {
+			local com_z = 1
+		}
+		//Recodage des réponses en 0, 1, 2, etc...
+		forvalues r = 0/`=`maxm_`j''-1'  {
+			qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
+			qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
+		}
+
+		// Vérif. Que toutes les modas sont utilisées & concordance entre temps
+		forvalues m = 0/`=`nbm_`j''-1' {
+			qui count if ``j'' == `m' & `gp' == 0
+			local nb_rn1_g0 = r(N)
+			qui count if ``j'' == `m' & `gp' == 1
+			local nb_rn1_g1 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m' & `gp' == 0
+			local nb_rn2_g0 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m' & `gp' == 1
+			local nb_rn2_g1 = r(N)
+			local nb_rn = min(`nb_rn1_g0',`nb_rn2_g0',`nb_rn1_g1',`nb_rn2_g1')
+
+			if `nb_rn' == 0 {		// Une moda n'est pas utilisée
+				local recoda_`j' = 1
+				if  `m' == 0 | `m' < `minm`j'_t1_g0' | `m' < `minm`j'_t2_g0' | `m' < `minm`j'_t1_g1' | `m' < `minm`j'_t2_g1' { // La moda 0 n'est pas utilisée
+					local stop = 1
+					forvalues k = 1/`=`nbm_`j''-`m'' {
+						qui count if ``j'' == `=`m' + `k'' & `gp' == 0
+						local v`k'1_0 = r(N)
+						qui count if ``j'' == `=`m' + `k'' & `gp' == 1
+						local v`k'1_1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
+						local v`k'2_0 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
+						local v`k'2_1 = r(N)
+						if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
+							qui replace ``j''= `=`m'+`k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else if `m' == `=`nbm_`j''-1' | `m' >= `maxm`j'_t2_g0' | `m' >= `maxm`j'_t1_g1' | `m' >= `maxm`j'_t2_g1' {	// La moda max n'est pas utilisée
+					local stop = 1
+					forvalues k = 1/`=`m'' {
+						qui count if ``j'' == `=`m' - `k'' & `gp' == 0
+						local v`k'1_0 = r(N)
+						qui count if ``j'' == `=`m' - `k'' & `gp' == 1
+						local v`k'1_1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
+						local v`k'2_0 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
+						local v`k'2_1 = r(N)
+						if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0 ) & `stop' != 0 {
+							qui replace ``j''= `=`m' - `k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''= `=`m' - `k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else {														// Moda central non utilisée
+					if runiform()>0.5{											// Tirage au sort pour regrouper
+						local stop = 1
+						forvalues k = 1/`m' {
+							qui count if ``j'' == `=`m' - `k'' & `gp' == 0
+							local v`k'1_0 = r(N)
+							qui count if ``j'' == `=`m' - `k'' & `gp' == 1
+							local v`k'1_1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
+							local v`k'2_0 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
+							local v`k'2_1 = r(N)
+							if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
+								qui replace ``j''= `=`m'-`k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m'-`k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+								local stop = 0
+							}
+						}
+					}
+					else {
+						local stop = 1
+						forvalues k = 1/`=`nbm_`j''-`m'' {
+							qui count if ``j'' == `=`m' + `k'' & `gp' == 0
+							local v`k'1_0 = r(N)
+							qui count if ``j'' == `=`m' + `k'' & `gp' == 1
+							local v`k'1_1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
+							local v`k'2_0 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
+							local v`k'2_1 = r(N)
+							if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0{
+								qui replace ``j''=`=`m' + `k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+								local stop = 0
+							}
+							else {
+								if `stop' != 0 {
+									qui replace ``j''= `nbm_`j'' if ``j''==`m'
+									qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
+									di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
+									local stop = 0
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+
+if `com_z' == 1 {
+	di
+	di "WARNING : Automatic recoding, the first response category is 0. see {help rosali:help rosali}."
+	di
+}
+
+forvalues j =1/`nbitems' {
+		qui tab ``j'', matrow(rec)					// Récupération des infos moda du temps 1
+		local nbm`j'_t1 = r(r)
+
+		qui tab ``=`j'+`nbitems''' 					// Récupération des infos moda du temps 2
+		local nbm`j'_t2 = r(r)
+
+	local nbm_`j' = max(`nbm`j'_t1', `nbm`j'_t2')
+	//Recodage des réponses en 0, 1, 2, etc...
+	forvalues r = 0/`=`nbm_`j''-1'  {
+		qui replace ``j'' = `r' if ``j'' == `=rec[`=`r'+1',1]'
+		qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=rec[`=`r'+1',1]'
+	}
+}
+
+/* Calcul de nbmoda & nbdif */
+forvalues j = 1/`nbitems' {
+	qui tab ``j''
+	local nbmoda_`j' = r(r)
+	local nbdif_`j' = r(r) - 1
+}
+
+local maxdif = 0
+local nbmoda_sum = 0
+forvalues j = 1/`nbitems' {
+	if `maxdif' < `nbdif_`j'' {
+		local maxdif = `nbdif_`j''
+	}
+	local nbmoda_sum = `nbmoda_sum' + `nbdif_`j''
+}
+
+/* Au moins 2 moda par item */
+forvalues j=1/`nbitems' {
+	if `nbmoda_`j'' == 1 {
+	di as error "``j'' have only one response category, the analysis can be performed only if each item has at least 2 response categories"
+		error 198
+		exit
+	}
+}
+
+local coln ""
+forvalues j =1 /`nbitems' {
+	local coln "`coln' ``j''"
+}
+
+matrix nbmod = J(2,`nbitems',.)
+
+matrix colnames nbmod = `coln'
+matrix rownames nbmod = NbModa Recoding
+
+forvalues j = 1/`nbitems' {
+	matrix nbmod[1,`j'] = `nbmoda_`j''
+	matrix nbmod[2,`j'] = `recoda_`j''
+}
+
+*Erreur si plus de 200 difficultés
+local nb_test = 0
+forvalues j=1/`nbitems' {
+	local nb_test = `nb_test'+`nbmoda_`j'' -1
+}
+
+if `nb_test' >= 200 {
+	di as error "The total number of items difficulties to be estimated must be less than 200 ({hi:moda} option option)."
+	error 198
+	exit
+}
+
+local nbitp = 0
+
+forvalues j = 1/`nbitems' {
+	if `nbmoda_`j'' >= 2 {
+		local nbitp = `nbitp' + 1
+	}
+}
+
+qui count
+local nbpat = r(N)
+
+
+/*********************************
+* AFFICHAGE INITIAL
+*********************************/
+di
+di _col(5) "{hline 78}"
+di  _col(15) "Time 1" _col(42) "Time 2" _col(65) "Nb of Answer Cat."
+di _col(5) "{hline 78}"
+forvalues j=1/`nbitems' {
+    di as text _col(15) abbrev("``j''",20)  _col(42) abbrev("``=`j'+`nbitems'''",20) _col(65) `nbmoda_`j''
+}
+di _col(5) "{hline 78}"
+if "`group'" != "" {
+	di _col(10) "Nb of patients: "  abbrev("`gp'",20) " 0 = `nbp_gp0' ;", abbrev("`gp'",20) " 1 = `nbp_gp1'"
+	di _col(5) "{hline 78}"
+}
+else {
+	di _col(10) "Nb. of patients: `nbpat'"
+	di _col(5) "{hline 78}"
+}
+di
+if `nbitems' == 1 {
+	di as error "The analysis can only be performed with at least 2 items."
+	error 198
+	exit
+}
+forvalues j = 1/`nbitems' {
+	if `nbmoda_`j'' == 2 {
+		di "WARNING: ``j'' has only 2 response categories, no distinction can be made between uniform or non-uniform recalibration."
+	}
+	if `nbmoda_`j'' == 1 {
+		di as error "Only `nbmoda_`j'' response categories of item ``j'' were used by the sample, the analysis cannot be performed."
+		error 198
+		exit
+	}
+	if `nbmoda_`j'' == 0 {
+		di as error "No response categories of item ``j'' were used by the sample, the analysis cannot be performed."
+		error 198
+		exit
+	}
+}
+di
+if "`group'" != "" {
+	di _col(2) as text "For all models : - mean of the latent trait in `gp' 0 at time 1 is constrained at 0"
+	di _col(19) "- equality of variances between groups"
+	di
+}
+else {
+	di _col(2) as text "For all models : mean of the latent trait at time 1 is constrained at 0"
+	di
+}
+
+
+
+/*********************************
+* DEFINITION DES CONTRAINTES
+*********************************/
+
+if "`group'"!="" { // Contraintes si option groupe
+	*EGALITE ENTRE GROUPES A T1 (1-200)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=0+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``j'']1.`gp'
+		}
+	}
+
+	*DIF UNIFORME A T1 (201-400)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=200+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp'=`p'*[1.``j'']1.`gp'-`p'*[1.``j'']0bn.`gp'
+		}
+	}
+
+	*EGALITES ENTRE T1 et T2, groupe 0 (401-600)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp'
+		}
+	}
+
+	*EGALITES ENTRE T1 et T2, groupe 1 (601-800)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=600+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'
+		}
+	}
+
+	* RC COMMUNE (801-1000)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=800+`maxdif'*(`j'-1)+`p'' [`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
+		}
+	}
+
+	* RC UNIFORME, groupe 0 (1001-1200)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'
+		}
+	}
+
+	* RC UNIFORME, groupe 1 (1201-1400)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1200+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
+		}
+	}
+
+	*Sans interaction temps x groupe
+	constraint 1999 [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'=[/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
+}
+else { //Contraintes si pas d'option groupe
+	*EGALITE ENTRE T1 et T2 (401-600)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']:_cons = [`p'.``=`j'+`nbitems''']:_cons
+		}
+	}
+	*RC UNIFORME (1001-1200)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']:_cons - [1.``j'']:_cons)=[`p'.``=`j'+`nbitems''']:_cons -[`p'.``j'']:_cons
+		}
+	}
+}
+
+/*********************************
+* MATRICE DES RESULTATS
+*********************************/
+matrix dif_rc=J(`nbitems',8,.)
+matrix colnames dif_rc=DIFT1 DIFU RC RC_DIF RCG0 RCUG0 RCG1 RCUG1
+local rown ""
+
+forvalues j =1 /`nbitems' {
+	local rown "`rown' ``j''"
+}
+matrix rownames dif_rc = `rown'
+
+*Nb modalité max
+local nbdif_max = 0
+forvalues j=1/`nbitems' {
+	if `nbdif_max' < `nbdif_`j'' {
+		local nbdif_max = `nbdif_`j''
+	}
+}
+
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////					PARTIE 1 : DIF A T1 ?						////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+
+if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "nodif"
+
+		di _dup(49) "_ "
+		di
+		di  as input "PART 1: DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS AT TIME 1"
+
+    *********************************
+  	**			MODEL B			   **
+  	*********************************
+
+  	local model ""
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			local model "`model' (`p'.``j''<-THETA@`p')"
+  		}
+  	}
+
+  	qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading cons) var(0: THETA@v) var(1:THETA@v) latent(THETA) nocapslatent
+  	/* Stockage des estimations du modèle */
+  	estimates store modeldifB
+  	matrix val_mB = r(table)
+  	matrix esti_B = e(b)
+
+
+  	/* Calcul des difficultés d'item (delta_j) */
+  	matrix delta_mB=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+  		}
+  	}
+  	local name_partOneL ""
+
+  	forvalues j=1/`nbitems' {
+  			local name_partOneL "`name_partOneL' ``j''"
+  	}
+
+  	matrix colnames delta_mB = `name_partOneC'
+  	matrix rownames delta_mB = `name_partOneL'
+  		matrix delta_mB_se=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC_se ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+  		}
+  	}
+
+  	matrix colnames delta_mB_se = `name_partOneC_se'
+  	matrix rownames delta_mB_se = `name_partOneL'
+
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			forvalues g=0/1{
+  				qui lincom -[`p'.``j'']:`g'.`gp'
+  				local delta`j'_`p'g`g'mB=r(estimate)
+  				local delta`j'_`p'g`g'mB_se=r(se)
+  				if `p'>1{
+  					qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+  					local delta`j'_`p'g`g'mB = r(estimate)
+  					local delta`j'_`p'g`g'mB_se = r(se)
+  				}
+  				matrix delta_mB[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB'
+  				matrix delta_mB_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB_se'
+  			}
+  		}
+  	}
+
+  	matrix var_mB = (val_mB[1,"/var(THETA)#0bn.`gp'"]\val_mB[2,"/var(THETA)#0bn.`gp'"])
+
+  	/*group effect*/
+  	qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
+  	local geffmB=r(estimate)
+  	local segeffmB=r(se)
+  	qui test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
+  	local gcmBp=r(p)
+  	local gcmBchi=r(chi2)
+  	local gcmBdf=r(df)
+
+
+  	*********************************
+  	**			MODEL A			   **
+  	*********************************
+
+  	local model ""
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			local model "`model' (`p'.``j''<-THETA@`p')"
+  		}
+  	}
+
+  	 qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading means) var(0: THETA@v) var(1:THETA@v) from(esti_B, skip) latent(THETA) nocapslatent
+
+  	/* Stockage des estimations du modèle */
+  	estimates store modeldifA
+  	matrix val_mA = r(table)
+  	matrix esti_A = e(b)
+
+  	/* Calcul des difficultés d'item (delta_j) */
+  	matrix delta_mA=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+  		}
+  	}
+  	local name_partOneL ""
+
+  	forvalues j=1/`nbitems' {
+  			local name_partOneL "`name_partOneL' ``j''"
+  	}
+  	matrix colnames delta_mA = `name_partOneC'
+  	matrix rownames delta_mA = `name_partOneL'
+  	matrix delta_mA_se=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC_se ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+  		}
+  	}
+
+  	matrix colnames delta_mA_se = `name_partOneC_se'
+  	matrix rownames delta_mA_se = `name_partOneL'
+
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			forvalues g=0/1{
+  				qui lincom -[`p'.``j'']:`g'.`gp'
+  				local delta`j'_`p'g`g'mA=r(estimate)
+  				local delta`j'_`p'g`g'mA_se=r(se)
+  				if `p'>1{
+  					qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+  					local delta`j'_`p'g`g'mA = r(estimate)
+  					local delta`j'_`p'g`g'mA_se = r(se)
+  				}
+  				matrix delta_mA[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA'
+  				matrix delta_mA_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA_se'
+  			}
+  		}
+  	}
+  	//Variance et se mA
+  	matrix var_mA = (val_mA[1,"/var(THETA)#0bn.`gp'"]\val_mA[2,"/var(THETA)#0bn.`gp'"])
+
+	*********************************
+	*************MODEL C*************
+	*********************************
+	// Etape itérative si lrtest significatif
+	local nb_stepC = 0
+  qui lrtest modeldifA modeldifB
+  local diftestp=r(p)
+	if `diftestp'<0.05{  /*If pvalue(LRtest)<0.05 then step C*/
+		di
+		di as input "PROCESSING STEP C"
+		di
+
+		/*test DIF pour chaque item*/
+		local boucle = 1
+		local stop = 0
+		while `boucle'<=`=`nbitp'-1' & `stop'==0{ /*on s'arrête quand on a libéré du DIF sur (tous les items-1) ou lorsqu'il n'y a plus de tests significatifs*/
+			local nb_stepC = `boucle'
+			local pajust=0.05
+			/*réinitialisation de la matrice de test*/
+			matrix test_difu_`boucle'=J(`nbitems',3,.)
+			matrix colnames test_difu_`boucle'=chi_DIFU df_DIFU pvalueDIFU
+			matrix test_dif_`boucle'=J(`nbitems',3,.)
+			matrix colnames test_dif_`boucle'=chi_DIF df_DIF pvalueDIF
+			local nbsig=0
+			local minpval=1
+			local itemdif=0
+			if "`detail'" != ""{
+
+				di as text "Loop `boucle'"
+				di as text _col(5) "Adjusted  alpha: " %6.4f  `pajust'
+				di
+				di as text _col(10) "{hline 65}"
+				di as text _col(10) "Freed item" _col(31) "Chi-Square"  _col(48) "DF" _col(57) "P-Value"
+				di as text _col(10) "{hline 65}"
+			}
+			/*boucle de test*/
+			forvalues j=1/`nbitems'{
+				//if `nbdif_`j'' > 2 {
+					local model ""
+					local listconst ""
+					if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF déjà détecté sur l'item j*/
+						/*on libère le DIF de l'item i: pas de contraintes*/
+						forvalues k=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes*/
+							if `k'!=`j' & `nbmoda_`j'' >= 2 {
+								if dif_rc[`k',1]==. | dif_rc[`k',1]==0 {/*pas de DIF sur item k: contraintes 1-200*/
+									forvalues p=1/`nbdif_`k''{
+										qui local listconst "`listconst' `=0+`maxdif'*(`k'-1)+`p''"
+										qui constraint list `=0+`maxdif'*(`k'-1)+`p''
+									}
+								}
+								else{
+									if dif_rc[`k',2]!=. & dif_rc[`k',2]!= 0 & `nbmoda_`k'' > 2 { /*DIF U: contraintes 201-400*/
+										forvalues p=2/`nbdif_`k''{
+											qui local listconst "`listconst' `=200+`maxdif'*(`k'-1)+`p''"
+											qui constraint list `=200+`maxdif'*(`k'-1)+`p''
+										}
+									}
+								}
+							}
+						}
+						forvalues jj=1/`nbitems'{
+							forvalues p=1/`nbdif_`jj''{
+								local model "`model' (`p'.``jj''<-THETA@`p')"
+							}
+						}
+						qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
+						estimates store modeldif3b`boucle'it`i'
+
+						*************************
+						*****test DIF item i*****
+						*************************
+						qui test [1.``j'']1.`gp'=[1.``j'']0bn.`gp'
+						if `nbmoda_`j'' > 2 {
+							forvalues p=2/`nbdif_`j''{
+								qui test [`p'.``j'']1.`gp'=[`p'.``j'']0bn.`gp', acc
+							}
+						}
+						matrix test_dif_`boucle'[`j',1]=(r(chi2),r(df),r(p))
+
+						/* Test DIF Uniforme */
+						if `nbmoda_`j'' > 2 {
+							qui test 2*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[2.``j'']1.`gp'-[2.``j'']0bn.`gp'
+							forvalues p=3/`nbdif_`j''{
+								qui test `p'*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp', acc
+							}
+							matrix test_difu_`boucle'[`j',1]=(r(chi2), r(df), r(p))
+						}
+
+						if test_dif_`boucle'[`j',3]<`pajust'{/*si DIF sur item i*/
+							local ++nbsig
+							if test_dif_`boucle'[`j',3]<`minpval'{
+								local minpval=test_dif_`boucle'[`j',3]
+								local itemdif=`j'
+							}
+						}
+						if "`detail'" != "" {
+							di as text  _col(10) abbrev("``j''",15)  as result _col(31) %6.3f test_dif_`boucle'[`j',1] _col(48) test_dif_`boucle'[`j',2] _col(57)  %6.4f test_dif_`boucle'[`j',3]
+						}
+					}
+			}
+			/*si nb de tests significatifs=0, on arrête*/
+			if `nbsig'==0{
+				local stop=1
+				if `boucle' == 1 {
+					if "`detail'" != "" {
+						di as text _col(10) "{hline 65}"
+						di
+						di as result  "No significant test: no difference between groups detected, no DIF detected"
+						di
+					}
+				}
+				else {
+					if "`detail'" != ""{
+						di as text _col(10) "{hline 65}"
+						di
+						di as result "No other significant tests"
+						di
+					}
+				}
+			}
+			else{/*si nb de tests significatifs>0, mise à jour de la matrice de résultats*/
+				matrix dif_rc[`itemdif',1]=`boucle'
+				if "`detail'" != ""{
+					di as text _col(10) "{hline 65}"
+					di
+					di as result "Difference between groups on ``itemdif'' at time 1"
+				}
+				if `nbmoda_`itemdif'' > 2 {
+					if "`detail'" != "" {
+
+						di
+						di %~60s as text "Test of uniform difference"
+						di _col(10) "{hline 40}"
+						di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
+						di _col(10) as result %4.2f `=test_difu_`boucle'[`itemdif',1]' _col(28) `=test_difu_`boucle'[`itemdif',2]' _col(40) %4.2f `=test_difu_`boucle'[`itemdif',3]'
+						di _col(10) as text "{hline 40}"
+					}
+					if test_difu_`boucle'[`itemdif',3]<0.05{	/*DIF NU détectée*/
+						matrix dif_rc[`itemdif',2]=0
+							di
+							di as result "``itemdif'' : Non-uniform differences of item difficulties between groups at T1"
+							di
+					}
+					else{/*DIF U détectée*/
+						matrix dif_rc[`itemdif',2]=`boucle'
+							di
+							di as result "``itemdif'' : Uniform differences of item difficulties between groups at T1"
+							di
+					}
+				}
+				else {
+					// Différence entre groupes au temps 1 mais slmt 2 moda. donc pas de U ou NU
+					di _col(15) _dup(60) "-"
+				}
+			}
+			local ++boucle
+		}
+	}
+
+	/* MODELE FINAL DE LA PARTIE 1. Si DIFT1 détecté (=Au moins 2 boucles dans l'étape C)*/
+	if `nb_stepC' > 1 {
+		forvalues j=1/`nbitems'{
+			local model ""
+			local listconst ""
+			if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF: contraintes 1-200*/
+				forvalues p=1/`nbdif_`j''{
+					qui local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
+					qui constraint list `=0+`maxdif'*(`j'-1)+`p''
+				}
+			}
+			else {
+				if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { /*DIF U: contraintes 201-400*/
+					forvalues p=2/`nbdif_`j''{
+						qui local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
+						qui constraint list `=200+`maxdif'*(`j'-1)+`p''
+					}
+				}
+			}
+		}
+		forvalues j=1/`nbitems'{
+			forvalues p=1/`nbdif_`j''{
+				local model "`model' (`p'.``j''<-THETA@`p')"
+			}
+		}
+
+		qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
+		/* Stockage des estimations du modèle */
+		estimates store modeldifCFin
+		matrix val_mC = r(table)
+
+		/* Calcul des difficultés d'item (delta_j) */
+		matrix delta_mCFin=J(`nbitems',`=`nbdif_max'*2',.)
+		local name_partOneC ""
+		forvalues p=1/`nbdif_max' {
+			forvalues g=0/1 {
+				local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+			}
+		}
+		local name_partOneL ""
+		forvalues j=1/`nbitems' {
+			local name_partOneL "`name_partOneL' ``j''"
+		}
+		matrix colnames delta_mCFin = `name_partOneC'
+		matrix rownames delta_mCFin = `name_partOneL'
+
+		matrix delta_mCFin_se=J(`nbitems',`=`nbdif_max'*2',.)
+		local name_partOneC_se ""
+
+		forvalues p=1/`nbdif_max' {
+			forvalues g=0/1 {
+				local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+			}
+		}
+		matrix colnames delta_mCFin_se = `name_partOneC_se'
+		matrix rownames delta_mCFin_se = `name_partOneL'
+
+		forvalues j=1/`nbitems'{
+			forvalues p=1/`nbdif_`j''{
+				forvalues g=0/1{
+					qui lincom -[`p'.``j'']:`g'.`gp'
+					local delta`j'_`p'g`g'mCFin=r(estimate)
+					local delta`j'_`p'g`g'mCFin_se=r(se)
+					if `p'>1{
+						qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+						local delta`j'_`p'g`g'mCFin = r(estimate)
+						local delta`j'_`p'g`g'mCFin_se = r(se)
+					}
+					matrix delta_mCFin[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin'
+					matrix delta_mCFin_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin_se'
+				}
+			}
+		}
+		if "`group'" != "" { //Variance et se mA
+			matrix var_mC = (val_mC[1,"/var(THETA)#0bn.`gp'"]\val_mC[2,"/var(THETA)#0bn.`gp'"])
+		}
+			/*group effect*/
+		qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
+		local geffmCFin=r(estimate)
+		local segeffmCFin=r(se)
+		qui	test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
+		local gcmCFinp=r(p)
+		local gcmCFinchi=r(chi2)
+		local gcmCFindf=r(df)
+	}
+}
+
+	*********************************
+	 ***		BILAN  			***
+	*********************************
+
+	if "`group'" != "" & "`nodif'" == "" {
+	di
+	di %~84s as result "SUMMARY"
+	di as result _col(2)  "{hline 80}"
+	di as result 		 _col(18) "Difference in"
+	di as result _col(2) "Item" _col(18) "groups at T1" _col(36) "Recalibration"  _col(54) "RC " abbrev("`gp'",10) " 0"  _col(72) "RC " abbrev("`gp'",10) " 1"
+	di as result _col(2) "{hline 80}"
+	forvalues j=1/`nbitems' {
+		local RC
+		local RCg0
+		local RCg1
+		local difft1
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
+			local RC "Common"
+		}
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
+			local RC "Differential"
+		}
+		if `nbmoda_`j'' > 2 {
+			if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+				local RCg0 "Uniform"
+			}
+			if (dif_rc[`j',6] == 0) {
+				local RCg0 "Non-uniform"
+			}
+				if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
+				local RCg1 "Uniform"
+			}
+			if ( dif_rc[`j',8] == 0) {
+				local RCg1 "Non-uniform"
+			}
+			if (dif_rc[`j',1] != . ) {
+				if (dif_rc[`j',2]!=0) {
+					local difft1 "Uniform"
+				}
+				else {
+					local difft1 "Non-uniform"
+				}
+			}
+		}
+		else {
+			if dif_rc[`j',6] != . {
+				local RCg0 " X "
+			}
+			if dif_rc[`j',8] != . {
+				local RCg1 " X "
+			}
+			if dif_rc[`j',1] != . {
+				local difft1 " X "
+			}
+		}
+		di as result _col(2) abbrev("``j''",15) as text _col(18) "`difft1'"  _col(36) "`RC'"  _col(54) "`RCg0'"  _col(72) "`RCg1'"
+	}
+	di as result _col(2) "{hline 80}"
+	di
+}
+else if "`group'" != "" & "`nodif'" != "" {
+	di
+	di %~90s as result "SUMMARY"
+	di as result _col(10)  "{hline 70}"
+	di as result _col(10) "Item" _col(26) "Recalibration"  _col(46) "RC `gp' 0"  _col(62) "RC `gp' 1"
+	di _col(10) "{hline 70}"
+	forvalues j=1/`nbitems' {
+		local RC
+		local RCg0
+		local RCg1
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
+			local RC "Common"
+		}
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
+			local RC "Differential"
+		}
+		if `nbmoda_`j'' > 2 {
+			if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+				local RCg0 "Uniform"
+			}
+			if (dif_rc[`j',6] == 0) {
+				local RCg0 "Non-uniform"
+			}
+			if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
+				local RCg1 "Uniform"
+			}
+			if ( dif_rc[`j',8] == 0) {
+				local RCg1 "Non-uniform"
+			}
+		}
+		else {
+			if dif_rc[`j',6] != . {
+				local RCg0 " X "
+			}
+			if dif_rc[`j',8] != . {
+				local RCg1 " X "
+			}
+		}
+		di as result _col(10) "``j''" as text  _col(26) "`RC'"  _col(44) "`RCg0'"  _col(62) "`RCg1'"
+	}
+	di as result _col(10) "{hline 70}"
+}
+else if "`group'" == "" {
+	di
+	di %~60s as result "SUMMARY"
+	di as result _col(10)  "{hline 40}"
+	di _col(10) "Item" _col(36) "Recalibration"
+	di _col(10) "{hline 40}"
+	forvalues j=1/`nbitems' {
+		local RC
+		if dif_rc[`j',3] != . {
+			if `nbmoda_`j'' > 2 {
+				if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+					local RC "Uniform"
+				}
+				if (dif_rc[`j',6] == 0) {
+					local RC "Non-uniform"
+				}
+			}
+			else {
+				local RC " X "
+			}
+		}
+		di as result _col(10) "``j''" as text  _col(38) "`RC'"
+	}
+	di as result _col(10) "{hline 40}"
+	di
+}
+
+matrix dif_detect = J(1,`nbitems',.)
+local numdif=1
+forvalues j=1/`nbitems' {
+  if dif_rc[`j',1] != . {
+    matrix dif_detect[1,`numdif']=`j'
+    local numdif = `numdif'+1
+  }
+}
+return matrix difitems = dif_detect
+
+capture qui use `saverspcm', clear
+
+end
diff --git a/Modules/rosali_custom/rosali_nolrt.ado b/Modules/rosali_custom/rosali_nolrt.ado
new file mode 100644
index 0000000..b2975ce
--- /dev/null
+++ b/Modules/rosali_custom/rosali_nolrt.ado
@@ -0,0 +1,1149 @@
+*! version 2.4 june2020
+*! Myriam Blanchin - Priscilla Brisson
+************************************************************************************************************
+* ROSALI: RespOnse-Shift ALgorithm at Item-level
+* Response-shift detection based on Rasch models family
+*
+* Version 1 : December 21, 2016 (Myriam Blanchin) /*rspcm122016*/
+* Version 1.1 : October 13, 2017 (Myriam Blanchin) /*option: MODA, automatic recoding of unused response categories*/
+* Version 2 : April, 2018 (Myriam Blanchin - Priscilla Brisson) /*option: GROUP, dichotomous group variable*/
+* Version 2.1 : October, 2018 (Myriam Blanchin - Priscilla Brisson) /* Version 1.1 + Version 2 */
+* Version 2.2 : February, 2019 (Priscilla Brisson) /* option nodif, optimization */
+* Version 2.3 : December, 2019 (Priscilla Brisson) /* option detail, + petites corrections */
+* Version 2.4 : June, 2020 (Myriam Blanchin) /* debug option detail + step C, modifs sorties et help */
+*
+* Myriam Blanchin, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* myriam.blanchin@univ-nantes.fr
+*
+* Priscilla Brisson, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* priscilla.brisson@univ-nantes.fr
+*
+* This program is free software; you can redistribute it and/or modify
+* it under the terms of the GNU General Public License as published by
+* the Free Software Foundation; either version 2 of the License, or
+* (at your option) any later version.
+*
+* This program is distributed in the hope that it will be useful,
+* but WITHOUT ANY WARRANTY; without even the implied warranty of
+* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+* GNU General Public License for more details.
+*
+* You should have received a copy of the GNU General Public License
+* along with this program; if not, write to the Free Software
+* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+************************************************************************************************************/
+
+program define rosali_nolrt, rclass
+
+timer clear 1
+timer on 1
+
+syntax varlist(min=2 numeric) [if] [,GROUP(varlist) NODIF PRO DETail]
+
+preserve
+version 15
+tempfile saverspcm
+capture qui save `saverspcm',replace
+local save1=_rc
+
+if "`if'"!="" {
+    qui keep `if'
+}
+
+if "`pro'" != "" {
+	di "START"
+}
+
+/**************************************************************************/
+set more off
+set matsize 5000
+constraint drop _all
+
+local gp "`group'"
+
+tokenize `varlist'
+local nbitems:word count `varlist'
+
+	/* Vérif nb d'items pair */
+local mod=mod(`nbitems',2)
+if `mod'!=0 {
+  di as error "You must enter an even number of items : the first half of the items represents the items at time 1 and the second half the items at time 2"
+  error 198
+  exit
+}
+
+local nbitems=`nbitems'/2
+
+
+if "`group'"=="" & "`nodif'"!="" {
+	di as error "nodif can only be used with the group option ({hi:nodif} option). Please correct this option."
+	error 198
+	exit
+}
+
+local nbc: word count `group'
+if `nbc' >= 2 {
+	di as error "Only one variable can be used for group option ({hi:group} option). Please correct this option."
+	error 198
+	exit
+}
+
+	/* Vérif qu'il y a 2 groupes si l'option groupe est choisie */
+if "`group'"!="" {
+  qui tab `group'
+  local nbgrp = r(r)
+  if `nbgrp' != 2 {
+	di as error "Only 2 groups are possible for the group option ({hi:group} option). Please correct this option."
+	error 420
+	exit
+  }
+}
+/* recoder la variable de groupe en 0, 1*/
+
+if "`group'"!="" {
+	qui tab `gp', matrow(rep)
+	qui matrix list rep
+	if rep[1,1]+rep[2,1] != 1 & rep[1,1]*rep[2,1] != 0 {
+		forvalues i=1/`=rowsof(rep)'{
+			qui replace `gp'=`i'-1 if `gp'==rep[`i',1]
+			di "WARNING : `gp' `=rep[`i',1]' is now `gp' `=`i'-1' "
+		}
+	}
+	forvalues g = 0/1 {
+		qui tab `gp' if `gp' == `g'
+		local nbp_gp`g' = r(N)
+	}
+}
+
+
+
+/*item rename*/
+/*
+Items au temps 1 : 1 à nbitems ``j''
+Items au temps 2 : nbitems à 2*nbitems ``=`j'+`nbitems'''
+
+Si t varie, puis num item : ``=(`t'-1)*`nbitems'+`j'''
+*/
+
+
+local com_z = 0 // Indicatrice de recodage
+	/*verif modalités répondues*/
+if "`gp'" == "" {							// Si pas d'option groupe
+	forvalues j = 1 / `nbitems' {
+		local recoda_`j' = 0
+		qui tab ``j'', matrow(rect1_`j')				// Récupération des infos moda du temps 1
+		local minm`j'_t1 = rect1_`j'[1,1]
+		local maxm`j'_t1 = rect1_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''', matrow(rect2_`j')			// Récupération des infos moda du temps 2
+		local minm`j'_t2 = rect2_`j'[1,1]
+		local maxm`j'_t2 = rect2_`j'[r(r),1]
+
+		local minm_`j' = min(`minm`j'_t1',`minm`j'_t2')		// Info moda pour l'item j
+		local maxm_`j' = max(`maxm`j'_t1',`maxm`j'_t2')
+		local nbm_`j' = `=`maxm_`j''-`minm_`j'''
+
+		if `minm_`j'' != 0 & `com_z' == 0 {
+			local com_z = 1
+		}
+
+
+		//Recodage des réponses en 0, 1, 2, etc...
+		forvalues r = 0/`=`maxm_`j''-1'  {
+				qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
+				qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
+		}
+
+		// Vérif. Que toutes les modas sont utilisées & concordance entre temps
+		forvalues m = 0/`nbm_`j'' {
+			qui count if ``j'' == `m'
+			local nb_rn1 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m'
+			local nb_rn2 = r(N)
+			local nb_rn = min(`nb_rn1',`nb_rn2')
+
+			if `nb_rn' == 0 {		// Une moda n'est pas utilisée
+				local recoda_`j' = 1
+				if   `m' == 0 | `m' <= `minm`j'_t1' | `m' <= `minm`j'_t2' { // La moda 0 ou les moda min ne sont pas utilisées
+					local stop = 1
+					forvalues k = 1/`=`nbm_`j''-`m'' {
+						qui count if ``j'' == `=`m' + `k''
+						local v`k'1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k''
+						local v`k'2 = r(N)
+						if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+							qui replace ``j''= `=`m'+`k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged "
+							local stop = 0
+						}
+					}
+				}
+				else if `m' >= `maxm`j'_t1' | `m' >= `maxm`j'_t2' | `m' == `maxm_`j'' {	// La (ou les) moda max ne sont pas utilisée(s)
+					local stop = 1
+					forvalues k = 1/`m' {
+						qui count if ``j'' == `=`m' - `k''
+						local v`k'1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k''
+						local v`k'2 = r(N)
+						if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+							qui replace ``j''=`=`m' - `k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m' - `k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else {
+					if runiform()>0.5{											// Tirage au sort pour regrouper
+						local stop = 1
+						forvalues k = 1/`m' {
+							qui count if ``j'' == `=`m' - `k''
+							local v`k'1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k''
+							local v`k'2 = r(N)
+							if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+								qui replace ``j''= `=`m'-`k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems''' =`=`m'-`k'' if ``=`j'+`nbitems''' ==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+								local stop = 0
+							}
+						}
+					}
+					else {
+						local stop = 1
+						forvalues k = 1/`=`nbm_`j''-`m'' {
+							qui count if ``j'' == `=`m' + `k''
+							local v`k'1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k''
+							local v`k'2 = r(N)
+							if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+								qui replace ``j''=`=`m' + `k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+								local stop = 0
+							}
+							else {
+								if `stop' != 0 {
+									qui replace ``j''= `nbm_`j'' if ``j''==`m'
+									qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
+									di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
+									local stop = 0
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+else {									// Cas où l'option groupe est utilisée
+	forvalues j = 1 / `nbitems' {
+		local recoda_`j' = 0
+		qui tab ``j'' if `gp' == 0, matrow(rect1_g0_`j') matcell(nbrt1_g0_`j')					// Récupération des infos moda du temps 1pour chaque groupe
+		local minm`j'_t1_g0 = rect1_g0_`j'[1,1]
+		local maxm`j'_t1_g0 = rect1_g0_`j'[r(r),1]
+
+		qui tab ``j'' if `gp' == 1, matrow(rect1_g1_`j') matcell(nbrt1_g1_`j')
+		local minm`j'_t1_g1 = rect1_g1_`j'[1,1]
+		local maxm`j'_t1_g1 = rect1_g1_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''' if `gp' == 0, matrow(rect2_g0_`j') matcell(nbrt2_g0_`j')				// Récupération des infos moda du temps 2 pour chaque groupe
+		local minm`j'_t2_g0 = rect2_g0_`j'[1,1]
+		local maxm`j'_t2_g0 = rect2_g0_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''' if `gp' == 1	, matrow(rect2_g1_`j') matcell(nbrt2_g1_`j')
+		local minm`j'_t2_g1 = rect2_g0_`j'[1,1]
+		local maxm`j'_t2_g1 = rect2_g0_`j'[r(r),1]
+
+		local minm_`j' = min(`minm`j'_t1_g0',`minm`j'_t2_g0',`minm`j'_t1_g1',`minm`j'_t2_g1')		// Info moda pour l'item j
+		local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t2_g0',`maxm`j'_t1_g1',`maxm`j'_t2_g1')
+		local nbm_`j' = `=`maxm_`j''-`minm_`j''+1'
+
+		if `minm_`j'' != 0 & `com_z' == 0 {
+			local com_z = 1
+		}
+		//Recodage des réponses en 0, 1, 2, etc...
+		forvalues r = 0/`=`maxm_`j''-1'  {
+			qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
+			qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
+		}
+
+		// Vérif. Que toutes les modas sont utilisées & concordance entre temps
+		forvalues m = 0/`=`nbm_`j''-1' {
+			qui count if ``j'' == `m' & `gp' == 0
+			local nb_rn1_g0 = r(N)
+			qui count if ``j'' == `m' & `gp' == 1
+			local nb_rn1_g1 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m' & `gp' == 0
+			local nb_rn2_g0 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m' & `gp' == 1
+			local nb_rn2_g1 = r(N)
+			local nb_rn = min(`nb_rn1_g0',`nb_rn2_g0',`nb_rn1_g1',`nb_rn2_g1')
+
+			if `nb_rn' == 0 {		// Une moda n'est pas utilisée
+				local recoda_`j' = 1
+				if  `m' == 0 | `m' < `minm`j'_t1_g0' | `m' < `minm`j'_t2_g0' | `m' < `minm`j'_t1_g1' | `m' < `minm`j'_t2_g1' { // La moda 0 n'est pas utilisée
+					local stop = 1
+					forvalues k = 1/`=`nbm_`j''-`m'' {
+						qui count if ``j'' == `=`m' + `k'' & `gp' == 0
+						local v`k'1_0 = r(N)
+						qui count if ``j'' == `=`m' + `k'' & `gp' == 1
+						local v`k'1_1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
+						local v`k'2_0 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
+						local v`k'2_1 = r(N)
+						if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
+							qui replace ``j''= `=`m'+`k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else if `m' == `=`nbm_`j''-1' | `m' >= `maxm`j'_t2_g0' | `m' >= `maxm`j'_t1_g1' | `m' >= `maxm`j'_t2_g1' {	// La moda max n'est pas utilisée
+					local stop = 1
+					forvalues k = 1/`=`m'' {
+						qui count if ``j'' == `=`m' - `k'' & `gp' == 0
+						local v`k'1_0 = r(N)
+						qui count if ``j'' == `=`m' - `k'' & `gp' == 1
+						local v`k'1_1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
+						local v`k'2_0 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
+						local v`k'2_1 = r(N)
+						if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0 ) & `stop' != 0 {
+							qui replace ``j''= `=`m' - `k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''= `=`m' - `k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else {														// Moda central non utilisée
+					if runiform()>0.5{											// Tirage au sort pour regrouper
+						local stop = 1
+						forvalues k = 1/`m' {
+							qui count if ``j'' == `=`m' - `k'' & `gp' == 0
+							local v`k'1_0 = r(N)
+							qui count if ``j'' == `=`m' - `k'' & `gp' == 1
+							local v`k'1_1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
+							local v`k'2_0 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
+							local v`k'2_1 = r(N)
+							if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
+								qui replace ``j''= `=`m'-`k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m'-`k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+								local stop = 0
+							}
+						}
+					}
+					else {
+						local stop = 1
+						forvalues k = 1/`=`nbm_`j''-`m'' {
+							qui count if ``j'' == `=`m' + `k'' & `gp' == 0
+							local v`k'1_0 = r(N)
+							qui count if ``j'' == `=`m' + `k'' & `gp' == 1
+							local v`k'1_1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
+							local v`k'2_0 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
+							local v`k'2_1 = r(N)
+							if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0{
+								qui replace ``j''=`=`m' + `k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+								local stop = 0
+							}
+							else {
+								if `stop' != 0 {
+									qui replace ``j''= `nbm_`j'' if ``j''==`m'
+									qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
+									di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
+									local stop = 0
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+
+if `com_z' == 1 {
+	di
+	di "WARNING : Automatic recoding, the first response category is 0. see {help rosali:help rosali}."
+	di
+}
+
+forvalues j =1/`nbitems' {
+		qui tab ``j'', matrow(rec)					// Récupération des infos moda du temps 1
+		local nbm`j'_t1 = r(r)
+
+		qui tab ``=`j'+`nbitems''' 					// Récupération des infos moda du temps 2
+		local nbm`j'_t2 = r(r)
+
+	local nbm_`j' = max(`nbm`j'_t1', `nbm`j'_t2')
+	//Recodage des réponses en 0, 1, 2, etc...
+	forvalues r = 0/`=`nbm_`j''-1'  {
+		qui replace ``j'' = `r' if ``j'' == `=rec[`=`r'+1',1]'
+		qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=rec[`=`r'+1',1]'
+	}
+}
+
+/* Calcul de nbmoda & nbdif */
+forvalues j = 1/`nbitems' {
+	qui tab ``j''
+	local nbmoda_`j' = r(r)
+	local nbdif_`j' = r(r) - 1
+}
+
+local maxdif = 0
+local nbmoda_sum = 0
+forvalues j = 1/`nbitems' {
+	if `maxdif' < `nbdif_`j'' {
+		local maxdif = `nbdif_`j''
+	}
+	local nbmoda_sum = `nbmoda_sum' + `nbdif_`j''
+}
+
+/* Au moins 2 moda par item */
+forvalues j=1/`nbitems' {
+	if `nbmoda_`j'' == 1 {
+	di as error "``j'' have only one response category, the analysis can be performed only if each item has at least 2 response categories"
+		error 198
+		exit
+	}
+}
+
+local coln ""
+forvalues j =1 /`nbitems' {
+	local coln "`coln' ``j''"
+}
+
+matrix nbmod = J(2,`nbitems',.)
+
+matrix colnames nbmod = `coln'
+matrix rownames nbmod = NbModa Recoding
+
+forvalues j = 1/`nbitems' {
+	matrix nbmod[1,`j'] = `nbmoda_`j''
+	matrix nbmod[2,`j'] = `recoda_`j''
+}
+
+*Erreur si plus de 200 difficultés
+local nb_test = 0
+forvalues j=1/`nbitems' {
+	local nb_test = `nb_test'+`nbmoda_`j'' -1
+}
+
+if `nb_test' >= 200 {
+	di as error "The total number of items difficulties to be estimated must be less than 200 ({hi:moda} option option)."
+	error 198
+	exit
+}
+
+local nbitp = 0
+
+forvalues j = 1/`nbitems' {
+	if `nbmoda_`j'' >= 2 {
+		local nbitp = `nbitp' + 1
+	}
+}
+
+qui count
+local nbpat = r(N)
+
+
+/*********************************
+* AFFICHAGE INITIAL
+*********************************/
+di
+di _col(5) "{hline 78}"
+di  _col(15) "Time 1" _col(42) "Time 2" _col(65) "Nb of Answer Cat."
+di _col(5) "{hline 78}"
+forvalues j=1/`nbitems' {
+    di as text _col(15) abbrev("``j''",20)  _col(42) abbrev("``=`j'+`nbitems'''",20) _col(65) `nbmoda_`j''
+}
+di _col(5) "{hline 78}"
+if "`group'" != "" {
+	di _col(10) "Nb of patients: "  abbrev("`gp'",20) " 0 = `nbp_gp0' ;", abbrev("`gp'",20) " 1 = `nbp_gp1'"
+	di _col(5) "{hline 78}"
+}
+else {
+	di _col(10) "Nb. of patients: `nbpat'"
+	di _col(5) "{hline 78}"
+}
+di
+if `nbitems' == 1 {
+	di as error "The analysis can only be performed with at least 2 items."
+	error 198
+	exit
+}
+forvalues j = 1/`nbitems' {
+	if `nbmoda_`j'' == 2 {
+		di "WARNING: ``j'' has only 2 response categories, no distinction can be made between uniform or non-uniform recalibration."
+	}
+	if `nbmoda_`j'' == 1 {
+		di as error "Only `nbmoda_`j'' response categories of item ``j'' were used by the sample, the analysis cannot be performed."
+		error 198
+		exit
+	}
+	if `nbmoda_`j'' == 0 {
+		di as error "No response categories of item ``j'' were used by the sample, the analysis cannot be performed."
+		error 198
+		exit
+	}
+}
+di
+if "`group'" != "" {
+	di _col(2) as text "For all models : - mean of the latent trait in `gp' 0 at time 1 is constrained at 0"
+	di _col(19) "- equality of variances between groups"
+	di
+}
+else {
+	di _col(2) as text "For all models : mean of the latent trait at time 1 is constrained at 0"
+	di
+}
+
+
+
+/*********************************
+* DEFINITION DES CONTRAINTES
+*********************************/
+
+if "`group'"!="" { // Contraintes si option groupe
+	*EGALITE ENTRE GROUPES A T1 (1-200)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=0+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``j'']1.`gp'
+		}
+	}
+
+	*DIF UNIFORME A T1 (201-400)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=200+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp'=`p'*[1.``j'']1.`gp'-`p'*[1.``j'']0bn.`gp'
+		}
+	}
+
+	*EGALITES ENTRE T1 et T2, groupe 0 (401-600)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp'
+		}
+	}
+
+	*EGALITES ENTRE T1 et T2, groupe 1 (601-800)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=600+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'
+		}
+	}
+
+	* RC COMMUNE (801-1000)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=800+`maxdif'*(`j'-1)+`p'' [`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
+		}
+	}
+
+	* RC UNIFORME, groupe 0 (1001-1200)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'
+		}
+	}
+
+	* RC UNIFORME, groupe 1 (1201-1400)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1200+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
+		}
+	}
+
+	*Sans interaction temps x groupe
+	constraint 1999 [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'=[/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
+}
+else { //Contraintes si pas d'option groupe
+	*EGALITE ENTRE T1 et T2 (401-600)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']:_cons = [`p'.``=`j'+`nbitems''']:_cons
+		}
+	}
+	*RC UNIFORME (1001-1200)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']:_cons - [1.``j'']:_cons)=[`p'.``=`j'+`nbitems''']:_cons -[`p'.``j'']:_cons
+		}
+	}
+}
+
+/*********************************
+* MATRICE DES RESULTATS
+*********************************/
+matrix dif_rc=J(`nbitems',8,.)
+matrix colnames dif_rc=DIFT1 DIFU RC RC_DIF RCG0 RCUG0 RCG1 RCUG1
+local rown ""
+
+forvalues j =1 /`nbitems' {
+	local rown "`rown' ``j''"
+}
+matrix rownames dif_rc = `rown'
+
+*Nb modalité max
+local nbdif_max = 0
+forvalues j=1/`nbitems' {
+	if `nbdif_max' < `nbdif_`j'' {
+		local nbdif_max = `nbdif_`j''
+	}
+}
+
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////					PARTIE 1 : DIF A T1 ?						////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+
+if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "nodif"
+
+		di _dup(49) "_ "
+		di
+		di  as input "PART 1: DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS AT TIME 1"
+
+    *********************************
+  	**			MODEL B			   **
+  	*********************************
+
+  	local model ""
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			local model "`model' (`p'.``j''<-THETA@`p')"
+  		}
+  	}
+
+  	qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading cons) var(0: THETA@v) var(1:THETA@v) latent(THETA) nocapslatent
+  	/* Stockage des estimations du modèle */
+  	estimates store modeldifB
+  	matrix val_mB = r(table)
+  	matrix esti_B = e(b)
+
+
+  	/* Calcul des difficultés d'item (delta_j) */
+  	matrix delta_mB=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+  		}
+  	}
+  	local name_partOneL ""
+
+  	forvalues j=1/`nbitems' {
+  			local name_partOneL "`name_partOneL' ``j''"
+  	}
+
+  	matrix colnames delta_mB = `name_partOneC'
+  	matrix rownames delta_mB = `name_partOneL'
+  		matrix delta_mB_se=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC_se ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+  		}
+  	}
+
+  	matrix colnames delta_mB_se = `name_partOneC_se'
+  	matrix rownames delta_mB_se = `name_partOneL'
+
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			forvalues g=0/1{
+  				qui lincom -[`p'.``j'']:`g'.`gp'
+  				local delta`j'_`p'g`g'mB=r(estimate)
+  				local delta`j'_`p'g`g'mB_se=r(se)
+  				if `p'>1{
+  					qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+  					local delta`j'_`p'g`g'mB = r(estimate)
+  					local delta`j'_`p'g`g'mB_se = r(se)
+  				}
+  				matrix delta_mB[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB'
+  				matrix delta_mB_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB_se'
+  			}
+  		}
+  	}
+
+  	matrix var_mB = (val_mB[1,"/var(THETA)#0bn.`gp'"]\val_mB[2,"/var(THETA)#0bn.`gp'"])
+
+  	/*group effect*/
+  	qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
+  	local geffmB=r(estimate)
+  	local segeffmB=r(se)
+  	qui test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
+  	local gcmBp=r(p)
+  	local gcmBchi=r(chi2)
+  	local gcmBdf=r(df)
+
+
+  	*********************************
+  	**			MODEL A			   **
+  	*********************************
+
+  	local model ""
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			local model "`model' (`p'.``j''<-THETA@`p')"
+  		}
+  	}
+
+  	 qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading means) var(0: THETA@v) var(1:THETA@v) from(esti_B, skip) latent(THETA) nocapslatent
+
+  	/* Stockage des estimations du modèle */
+  	estimates store modeldifA
+  	matrix val_mA = r(table)
+  	matrix esti_A = e(b)
+
+  	/* Calcul des difficultés d'item (delta_j) */
+  	matrix delta_mA=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+  		}
+  	}
+  	local name_partOneL ""
+
+  	forvalues j=1/`nbitems' {
+  			local name_partOneL "`name_partOneL' ``j''"
+  	}
+  	matrix colnames delta_mA = `name_partOneC'
+  	matrix rownames delta_mA = `name_partOneL'
+  	matrix delta_mA_se=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC_se ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+  		}
+  	}
+
+  	matrix colnames delta_mA_se = `name_partOneC_se'
+  	matrix rownames delta_mA_se = `name_partOneL'
+
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			forvalues g=0/1{
+  				qui lincom -[`p'.``j'']:`g'.`gp'
+  				local delta`j'_`p'g`g'mA=r(estimate)
+  				local delta`j'_`p'g`g'mA_se=r(se)
+  				if `p'>1{
+  					qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+  					local delta`j'_`p'g`g'mA = r(estimate)
+  					local delta`j'_`p'g`g'mA_se = r(se)
+  				}
+  				matrix delta_mA[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA'
+  				matrix delta_mA_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA_se'
+  			}
+  		}
+  	}
+  	//Variance et se mA
+  	matrix var_mA = (val_mA[1,"/var(THETA)#0bn.`gp'"]\val_mA[2,"/var(THETA)#0bn.`gp'"])
+
+	*********************************
+	*************MODEL C*************
+	*********************************
+	// Etape itérative si lrtest significatif
+	local nb_stepC = 0
+  local diftestp = 1
+	if `diftestp'<2{  /*If pvalue(LRtest)<0.05 then step C*/
+		di
+		di as input "PROCESSING STEP C"
+		di
+
+		/*test DIF pour chaque item*/
+		local boucle = 1
+		local stop = 0
+		while `boucle'<=`=`nbitp'-1' & `stop'==0{ /*on s'arrête quand on a libéré du DIF sur (tous les items-1) ou lorsqu'il n'y a plus de tests significatifs*/
+			local nb_stepC = `boucle'
+			local pajust=0.05/`=`nbitp'+1-`boucle''
+			/*réinitialisation de la matrice de test*/
+			matrix test_difu_`boucle'=J(`nbitems',3,.)
+			matrix colnames test_difu_`boucle'=chi_DIFU df_DIFU pvalueDIFU
+			matrix test_dif_`boucle'=J(`nbitems',3,.)
+			matrix colnames test_dif_`boucle'=chi_DIF df_DIF pvalueDIF
+			local nbsig=0
+			local minpval=1
+			local itemdif=0
+			if "`detail'" != ""{
+
+				di as text "Loop `boucle'"
+				di as text _col(5) "Adjusted  alpha: " %6.4f  `pajust'
+				di
+				di as text _col(10) "{hline 65}"
+				di as text _col(10) "Freed item" _col(31) "Chi-Square"  _col(48) "DF" _col(57) "P-Value"
+				di as text _col(10) "{hline 65}"
+			}
+			/*boucle de test*/
+			forvalues j=1/`nbitems'{
+				//if `nbdif_`j'' > 2 {
+					local model ""
+					local listconst ""
+					if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF déjà détecté sur l'item j*/
+						/*on libère le DIF de l'item i: pas de contraintes*/
+						forvalues k=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes*/
+							if `k'!=`j' & `nbmoda_`j'' >= 2 {
+								if dif_rc[`k',1]==. | dif_rc[`k',1]==0 {/*pas de DIF sur item k: contraintes 1-200*/
+									forvalues p=1/`nbdif_`k''{
+										qui local listconst "`listconst' `=0+`maxdif'*(`k'-1)+`p''"
+										qui constraint list `=0+`maxdif'*(`k'-1)+`p''
+									}
+								}
+								else{
+									if dif_rc[`k',2]!=. & dif_rc[`k',2]!= 0 & `nbmoda_`k'' > 2 { /*DIF U: contraintes 201-400*/
+										forvalues p=2/`nbdif_`k''{
+											qui local listconst "`listconst' `=200+`maxdif'*(`k'-1)+`p''"
+											qui constraint list `=200+`maxdif'*(`k'-1)+`p''
+										}
+									}
+								}
+							}
+						}
+						forvalues jj=1/`nbitems'{
+							forvalues p=1/`nbdif_`jj''{
+								local model "`model' (`p'.``jj''<-THETA@`p')"
+							}
+						}
+						qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
+						estimates store modeldif3b`boucle'it`i'
+
+						*************************
+						*****test DIF item i*****
+						*************************
+						qui test [1.``j'']1.`gp'=[1.``j'']0bn.`gp'
+						if `nbmoda_`j'' > 2 {
+							forvalues p=2/`nbdif_`j''{
+								qui test [`p'.``j'']1.`gp'=[`p'.``j'']0bn.`gp', acc
+							}
+						}
+						matrix test_dif_`boucle'[`j',1]=(r(chi2),r(df),r(p))
+
+						/* Test DIF Uniforme */
+						if `nbmoda_`j'' > 2 {
+							qui test 2*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[2.``j'']1.`gp'-[2.``j'']0bn.`gp'
+							forvalues p=3/`nbdif_`j''{
+								qui test `p'*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp', acc
+							}
+							matrix test_difu_`boucle'[`j',1]=(r(chi2), r(df), r(p))
+						}
+
+						if test_dif_`boucle'[`j',3]<`pajust'{/*si DIF sur item i*/
+							local ++nbsig
+							if test_dif_`boucle'[`j',3]<`minpval'{
+								local minpval=test_dif_`boucle'[`j',3]
+								local itemdif=`j'
+							}
+						}
+						if "`detail'" != "" {
+							di as text  _col(10) abbrev("``j''",15)  as result _col(31) %6.3f test_dif_`boucle'[`j',1] _col(48) test_dif_`boucle'[`j',2] _col(57)  %6.4f test_dif_`boucle'[`j',3]
+						}
+					}
+			}
+			/*si nb de tests significatifs=0, on arrête*/
+			if `nbsig'==0{
+				local stop=1
+				if `boucle' == 1 {
+					if "`detail'" != "" {
+						di as text _col(10) "{hline 65}"
+						di
+						di as result  "No significant test: no difference between groups detected, no DIF detected"
+						di
+					}
+				}
+				else {
+					if "`detail'" != ""{
+						di as text _col(10) "{hline 65}"
+						di
+						di as result "No other significant tests"
+						di
+					}
+				}
+			}
+			else{/*si nb de tests significatifs>0, mise à jour de la matrice de résultats*/
+				matrix dif_rc[`itemdif',1]=`boucle'
+				if "`detail'" != ""{
+					di as text _col(10) "{hline 65}"
+					di
+					di as result "Difference between groups on ``itemdif'' at time 1"
+				}
+				if `nbmoda_`itemdif'' > 2 {
+					if "`detail'" != "" {
+
+						di
+						di %~60s as text "Test of uniform difference"
+						di _col(10) "{hline 40}"
+						di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
+						di _col(10) as result %4.2f `=test_difu_`boucle'[`itemdif',1]' _col(28) `=test_difu_`boucle'[`itemdif',2]' _col(40) %4.2f `=test_difu_`boucle'[`itemdif',3]'
+						di _col(10) as text "{hline 40}"
+					}
+					if test_difu_`boucle'[`itemdif',3]<0.05{	/*DIF NU détectée*/
+						matrix dif_rc[`itemdif',2]=0
+							di
+							di as result "``itemdif'' : Non-uniform differences of item difficulties between groups at T1"
+							di
+					}
+					else{/*DIF U détectée*/
+						matrix dif_rc[`itemdif',2]=`boucle'
+							di
+							di as result "``itemdif'' : Uniform differences of item difficulties between groups at T1"
+							di
+					}
+				}
+				else {
+					// Différence entre groupes au temps 1 mais slmt 2 moda. donc pas de U ou NU
+					di _col(15) _dup(60) "-"
+				}
+			}
+			local ++boucle
+		}
+	}
+
+	/* MODELE FINAL DE LA PARTIE 1. Si DIFT1 détecté (=Au moins 2 boucles dans l'étape C)*/
+	if `nb_stepC' > 1 {
+		forvalues j=1/`nbitems'{
+			local model ""
+			local listconst ""
+			if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF: contraintes 1-200*/
+				forvalues p=1/`nbdif_`j''{
+					qui local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
+					qui constraint list `=0+`maxdif'*(`j'-1)+`p''
+				}
+			}
+			else {
+				if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { /*DIF U: contraintes 201-400*/
+					forvalues p=2/`nbdif_`j''{
+						qui local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
+						qui constraint list `=200+`maxdif'*(`j'-1)+`p''
+					}
+				}
+			}
+		}
+		forvalues j=1/`nbitems'{
+			forvalues p=1/`nbdif_`j''{
+				local model "`model' (`p'.``j''<-THETA@`p')"
+			}
+		}
+
+		qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
+		/* Stockage des estimations du modèle */
+		estimates store modeldifCFin
+		matrix val_mC = r(table)
+
+		/* Calcul des difficultés d'item (delta_j) */
+		matrix delta_mCFin=J(`nbitems',`=`nbdif_max'*2',.)
+		local name_partOneC ""
+		forvalues p=1/`nbdif_max' {
+			forvalues g=0/1 {
+				local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+			}
+		}
+		local name_partOneL ""
+		forvalues j=1/`nbitems' {
+			local name_partOneL "`name_partOneL' ``j''"
+		}
+		matrix colnames delta_mCFin = `name_partOneC'
+		matrix rownames delta_mCFin = `name_partOneL'
+
+		matrix delta_mCFin_se=J(`nbitems',`=`nbdif_max'*2',.)
+		local name_partOneC_se ""
+
+		forvalues p=1/`nbdif_max' {
+			forvalues g=0/1 {
+				local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+			}
+		}
+		matrix colnames delta_mCFin_se = `name_partOneC_se'
+		matrix rownames delta_mCFin_se = `name_partOneL'
+
+		forvalues j=1/`nbitems'{
+			forvalues p=1/`nbdif_`j''{
+				forvalues g=0/1{
+					qui lincom -[`p'.``j'']:`g'.`gp'
+					local delta`j'_`p'g`g'mCFin=r(estimate)
+					local delta`j'_`p'g`g'mCFin_se=r(se)
+					if `p'>1{
+						qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+						local delta`j'_`p'g`g'mCFin = r(estimate)
+						local delta`j'_`p'g`g'mCFin_se = r(se)
+					}
+					matrix delta_mCFin[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin'
+					matrix delta_mCFin_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin_se'
+				}
+			}
+		}
+		if "`group'" != "" { //Variance et se mA
+			matrix var_mC = (val_mC[1,"/var(THETA)#0bn.`gp'"]\val_mC[2,"/var(THETA)#0bn.`gp'"])
+		}
+			/*group effect*/
+		qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
+		local geffmCFin=r(estimate)
+		local segeffmCFin=r(se)
+		qui	test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
+		local gcmCFinp=r(p)
+		local gcmCFinchi=r(chi2)
+		local gcmCFindf=r(df)
+	}
+}
+
+	*********************************
+	 ***		BILAN  			***
+	*********************************
+
+	if "`group'" != "" & "`nodif'" == "" {
+	di
+	di %~84s as result "SUMMARY"
+	di as result _col(2)  "{hline 80}"
+	di as result 		 _col(18) "Difference in"
+	di as result _col(2) "Item" _col(18) "groups at T1" _col(36) "Recalibration"  _col(54) "RC " abbrev("`gp'",10) " 0"  _col(72) "RC " abbrev("`gp'",10) " 1"
+	di as result _col(2) "{hline 80}"
+	forvalues j=1/`nbitems' {
+		local RC
+		local RCg0
+		local RCg1
+		local difft1
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
+			local RC "Common"
+		}
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
+			local RC "Differential"
+		}
+		if `nbmoda_`j'' > 2 {
+			if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+				local RCg0 "Uniform"
+			}
+			if (dif_rc[`j',6] == 0) {
+				local RCg0 "Non-uniform"
+			}
+				if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
+				local RCg1 "Uniform"
+			}
+			if ( dif_rc[`j',8] == 0) {
+				local RCg1 "Non-uniform"
+			}
+			if (dif_rc[`j',1] != . ) {
+				if (dif_rc[`j',2]!=0) {
+					local difft1 "Uniform"
+				}
+				else {
+					local difft1 "Non-uniform"
+				}
+			}
+		}
+		else {
+			if dif_rc[`j',6] != . {
+				local RCg0 " X "
+			}
+			if dif_rc[`j',8] != . {
+				local RCg1 " X "
+			}
+			if dif_rc[`j',1] != . {
+				local difft1 " X "
+			}
+		}
+		di as result _col(2) abbrev("``j''",15) as text _col(18) "`difft1'"  _col(36) "`RC'"  _col(54) "`RCg0'"  _col(72) "`RCg1'"
+	}
+	di as result _col(2) "{hline 80}"
+	di
+}
+else if "`group'" != "" & "`nodif'" != "" {
+	di
+	di %~90s as result "SUMMARY"
+	di as result _col(10)  "{hline 70}"
+	di as result _col(10) "Item" _col(26) "Recalibration"  _col(46) "RC `gp' 0"  _col(62) "RC `gp' 1"
+	di _col(10) "{hline 70}"
+	forvalues j=1/`nbitems' {
+		local RC
+		local RCg0
+		local RCg1
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
+			local RC "Common"
+		}
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
+			local RC "Differential"
+		}
+		if `nbmoda_`j'' > 2 {
+			if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+				local RCg0 "Uniform"
+			}
+			if (dif_rc[`j',6] == 0) {
+				local RCg0 "Non-uniform"
+			}
+			if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
+				local RCg1 "Uniform"
+			}
+			if ( dif_rc[`j',8] == 0) {
+				local RCg1 "Non-uniform"
+			}
+		}
+		else {
+			if dif_rc[`j',6] != . {
+				local RCg0 " X "
+			}
+			if dif_rc[`j',8] != . {
+				local RCg1 " X "
+			}
+		}
+		di as result _col(10) "``j''" as text  _col(26) "`RC'"  _col(44) "`RCg0'"  _col(62) "`RCg1'"
+	}
+	di as result _col(10) "{hline 70}"
+}
+else if "`group'" == "" {
+	di
+	di %~60s as result "SUMMARY"
+	di as result _col(10)  "{hline 40}"
+	di _col(10) "Item" _col(36) "Recalibration"
+	di _col(10) "{hline 40}"
+	forvalues j=1/`nbitems' {
+		local RC
+		if dif_rc[`j',3] != . {
+			if `nbmoda_`j'' > 2 {
+				if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+					local RC "Uniform"
+				}
+				if (dif_rc[`j',6] == 0) {
+					local RC "Non-uniform"
+				}
+			}
+			else {
+				local RC " X "
+			}
+		}
+		di as result _col(10) "``j''" as text  _col(38) "`RC'"
+	}
+	di as result _col(10) "{hline 40}"
+	di
+}
+
+matrix dif_detect = J(1,`nbitems',.)
+local numdif=1
+forvalues j=1/`nbitems' {
+  if dif_rc[`j',1] != . {
+    matrix dif_detect[1,`numdif']=`j'
+    local numdif = `numdif'+1
+  }
+}
+return matrix difitems = dif_detect
+
+capture qui use `saverspcm', clear
+
+end
diff --git a/Modules/rosali_custom/rosali_nolrt_nobf.ado b/Modules/rosali_custom/rosali_nolrt_nobf.ado
new file mode 100644
index 0000000..8f273aa
--- /dev/null
+++ b/Modules/rosali_custom/rosali_nolrt_nobf.ado
@@ -0,0 +1,1149 @@
+*! version 2.4 june2020
+*! Myriam Blanchin - Priscilla Brisson
+************************************************************************************************************
+* ROSALI: RespOnse-Shift ALgorithm at Item-level
+* Response-shift detection based on Rasch models family
+*
+* Version 1 : December 21, 2016 (Myriam Blanchin) /*rspcm122016*/
+* Version 1.1 : October 13, 2017 (Myriam Blanchin) /*option: MODA, automatic recoding of unused response categories*/
+* Version 2 : April, 2018 (Myriam Blanchin - Priscilla Brisson) /*option: GROUP, dichotomous group variable*/
+* Version 2.1 : October, 2018 (Myriam Blanchin - Priscilla Brisson) /* Version 1.1 + Version 2 */
+* Version 2.2 : February, 2019 (Priscilla Brisson) /* option nodif, optimization */
+* Version 2.3 : December, 2019 (Priscilla Brisson) /* option detail, + petites corrections */
+* Version 2.4 : June, 2020 (Myriam Blanchin) /* debug option detail + step C, modifs sorties et help */
+*
+* Myriam Blanchin, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* myriam.blanchin@univ-nantes.fr
+*
+* Priscilla Brisson, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* priscilla.brisson@univ-nantes.fr
+*
+* This program is free software; you can redistribute it and/or modify
+* it under the terms of the GNU General Public License as published by
+* the Free Software Foundation; either version 2 of the License, or
+* (at your option) any later version.
+*
+* This program is distributed in the hope that it will be useful,
+* but WITHOUT ANY WARRANTY; without even the implied warranty of
+* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+* GNU General Public License for more details.
+*
+* You should have received a copy of the GNU General Public License
+* along with this program; if not, write to the Free Software
+* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+************************************************************************************************************/
+
+program define rosali_nolrt_nobf, rclass
+
+timer clear 1
+timer on 1
+
+syntax varlist(min=2 numeric) [if] [,GROUP(varlist) NODIF PRO DETail]
+
+preserve
+version 15
+tempfile saverspcm
+capture qui save `saverspcm',replace
+local save1=_rc
+
+if "`if'"!="" {
+    qui keep `if'
+}
+
+if "`pro'" != "" {
+	di "START"
+}
+
+/**************************************************************************/
+set more off
+set matsize 5000
+constraint drop _all
+
+local gp "`group'"
+
+tokenize `varlist'
+local nbitems:word count `varlist'
+
+	/* Vérif nb d'items pair */
+local mod=mod(`nbitems',2)
+if `mod'!=0 {
+  di as error "You must enter an even number of items : the first half of the items represents the items at time 1 and the second half the items at time 2"
+  error 198
+  exit
+}
+
+local nbitems=`nbitems'/2
+
+
+if "`group'"=="" & "`nodif'"!="" {
+	di as error "nodif can only be used with the group option ({hi:nodif} option). Please correct this option."
+	error 198
+	exit
+}
+
+local nbc: word count `group'
+if `nbc' >= 2 {
+	di as error "Only one variable can be used for group option ({hi:group} option). Please correct this option."
+	error 198
+	exit
+}
+
+	/* Vérif qu'il y a 2 groupes si l'option groupe est choisie */
+if "`group'"!="" {
+  qui tab `group'
+  local nbgrp = r(r)
+  if `nbgrp' != 2 {
+	di as error "Only 2 groups are possible for the group option ({hi:group} option). Please correct this option."
+	error 420
+	exit
+  }
+}
+/* recoder la variable de groupe en 0, 1*/
+
+if "`group'"!="" {
+	qui tab `gp', matrow(rep)
+	qui matrix list rep
+	if rep[1,1]+rep[2,1] != 1 & rep[1,1]*rep[2,1] != 0 {
+		forvalues i=1/`=rowsof(rep)'{
+			qui replace `gp'=`i'-1 if `gp'==rep[`i',1]
+			di "WARNING : `gp' `=rep[`i',1]' is now `gp' `=`i'-1' "
+		}
+	}
+	forvalues g = 0/1 {
+		qui tab `gp' if `gp' == `g'
+		local nbp_gp`g' = r(N)
+	}
+}
+
+
+
+/*item rename*/
+/*
+Items au temps 1 : 1 à nbitems ``j''
+Items au temps 2 : nbitems à 2*nbitems ``=`j'+`nbitems'''
+
+Si t varie, puis num item : ``=(`t'-1)*`nbitems'+`j'''
+*/
+
+
+local com_z = 0 // Indicatrice de recodage
+	/*verif modalités répondues*/
+if "`gp'" == "" {							// Si pas d'option groupe
+	forvalues j = 1 / `nbitems' {
+		local recoda_`j' = 0
+		qui tab ``j'', matrow(rect1_`j')				// Récupération des infos moda du temps 1
+		local minm`j'_t1 = rect1_`j'[1,1]
+		local maxm`j'_t1 = rect1_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''', matrow(rect2_`j')			// Récupération des infos moda du temps 2
+		local minm`j'_t2 = rect2_`j'[1,1]
+		local maxm`j'_t2 = rect2_`j'[r(r),1]
+
+		local minm_`j' = min(`minm`j'_t1',`minm`j'_t2')		// Info moda pour l'item j
+		local maxm_`j' = max(`maxm`j'_t1',`maxm`j'_t2')
+		local nbm_`j' = `=`maxm_`j''-`minm_`j'''
+
+		if `minm_`j'' != 0 & `com_z' == 0 {
+			local com_z = 1
+		}
+
+
+		//Recodage des réponses en 0, 1, 2, etc...
+		forvalues r = 0/`=`maxm_`j''-1'  {
+				qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
+				qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
+		}
+
+		// Vérif. Que toutes les modas sont utilisées & concordance entre temps
+		forvalues m = 0/`nbm_`j'' {
+			qui count if ``j'' == `m'
+			local nb_rn1 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m'
+			local nb_rn2 = r(N)
+			local nb_rn = min(`nb_rn1',`nb_rn2')
+
+			if `nb_rn' == 0 {		// Une moda n'est pas utilisée
+				local recoda_`j' = 1
+				if   `m' == 0 | `m' <= `minm`j'_t1' | `m' <= `minm`j'_t2' { // La moda 0 ou les moda min ne sont pas utilisées
+					local stop = 1
+					forvalues k = 1/`=`nbm_`j''-`m'' {
+						qui count if ``j'' == `=`m' + `k''
+						local v`k'1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k''
+						local v`k'2 = r(N)
+						if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+							qui replace ``j''= `=`m'+`k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged "
+							local stop = 0
+						}
+					}
+				}
+				else if `m' >= `maxm`j'_t1' | `m' >= `maxm`j'_t2' | `m' == `maxm_`j'' {	// La (ou les) moda max ne sont pas utilisée(s)
+					local stop = 1
+					forvalues k = 1/`m' {
+						qui count if ``j'' == `=`m' - `k''
+						local v`k'1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k''
+						local v`k'2 = r(N)
+						if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+							qui replace ``j''=`=`m' - `k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m' - `k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else {
+					if runiform()>0.5{											// Tirage au sort pour regrouper
+						local stop = 1
+						forvalues k = 1/`m' {
+							qui count if ``j'' == `=`m' - `k''
+							local v`k'1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k''
+							local v`k'2 = r(N)
+							if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+								qui replace ``j''= `=`m'-`k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems''' =`=`m'-`k'' if ``=`j'+`nbitems''' ==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+								local stop = 0
+							}
+						}
+					}
+					else {
+						local stop = 1
+						forvalues k = 1/`=`nbm_`j''-`m'' {
+							qui count if ``j'' == `=`m' + `k''
+							local v`k'1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k''
+							local v`k'2 = r(N)
+							if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+								qui replace ``j''=`=`m' + `k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+								local stop = 0
+							}
+							else {
+								if `stop' != 0 {
+									qui replace ``j''= `nbm_`j'' if ``j''==`m'
+									qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
+									di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
+									local stop = 0
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+else {									// Cas où l'option groupe est utilisée
+	forvalues j = 1 / `nbitems' {
+		local recoda_`j' = 0
+		qui tab ``j'' if `gp' == 0, matrow(rect1_g0_`j') matcell(nbrt1_g0_`j')					// Récupération des infos moda du temps 1pour chaque groupe
+		local minm`j'_t1_g0 = rect1_g0_`j'[1,1]
+		local maxm`j'_t1_g0 = rect1_g0_`j'[r(r),1]
+
+		qui tab ``j'' if `gp' == 1, matrow(rect1_g1_`j') matcell(nbrt1_g1_`j')
+		local minm`j'_t1_g1 = rect1_g1_`j'[1,1]
+		local maxm`j'_t1_g1 = rect1_g1_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''' if `gp' == 0, matrow(rect2_g0_`j') matcell(nbrt2_g0_`j')				// Récupération des infos moda du temps 2 pour chaque groupe
+		local minm`j'_t2_g0 = rect2_g0_`j'[1,1]
+		local maxm`j'_t2_g0 = rect2_g0_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''' if `gp' == 1	, matrow(rect2_g1_`j') matcell(nbrt2_g1_`j')
+		local minm`j'_t2_g1 = rect2_g0_`j'[1,1]
+		local maxm`j'_t2_g1 = rect2_g0_`j'[r(r),1]
+
+		local minm_`j' = min(`minm`j'_t1_g0',`minm`j'_t2_g0',`minm`j'_t1_g1',`minm`j'_t2_g1')		// Info moda pour l'item j
+		local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t2_g0',`maxm`j'_t1_g1',`maxm`j'_t2_g1')
+		local nbm_`j' = `=`maxm_`j''-`minm_`j''+1'
+
+		if `minm_`j'' != 0 & `com_z' == 0 {
+			local com_z = 1
+		}
+		//Recodage des réponses en 0, 1, 2, etc...
+		forvalues r = 0/`=`maxm_`j''-1'  {
+			qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
+			qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
+		}
+
+		// Vérif. Que toutes les modas sont utilisées & concordance entre temps
+		forvalues m = 0/`=`nbm_`j''-1' {
+			qui count if ``j'' == `m' & `gp' == 0
+			local nb_rn1_g0 = r(N)
+			qui count if ``j'' == `m' & `gp' == 1
+			local nb_rn1_g1 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m' & `gp' == 0
+			local nb_rn2_g0 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m' & `gp' == 1
+			local nb_rn2_g1 = r(N)
+			local nb_rn = min(`nb_rn1_g0',`nb_rn2_g0',`nb_rn1_g1',`nb_rn2_g1')
+
+			if `nb_rn' == 0 {		// Une moda n'est pas utilisée
+				local recoda_`j' = 1
+				if  `m' == 0 | `m' < `minm`j'_t1_g0' | `m' < `minm`j'_t2_g0' | `m' < `minm`j'_t1_g1' | `m' < `minm`j'_t2_g1' { // La moda 0 n'est pas utilisée
+					local stop = 1
+					forvalues k = 1/`=`nbm_`j''-`m'' {
+						qui count if ``j'' == `=`m' + `k'' & `gp' == 0
+						local v`k'1_0 = r(N)
+						qui count if ``j'' == `=`m' + `k'' & `gp' == 1
+						local v`k'1_1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
+						local v`k'2_0 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
+						local v`k'2_1 = r(N)
+						if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
+							qui replace ``j''= `=`m'+`k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else if `m' == `=`nbm_`j''-1' | `m' >= `maxm`j'_t2_g0' | `m' >= `maxm`j'_t1_g1' | `m' >= `maxm`j'_t2_g1' {	// La moda max n'est pas utilisée
+					local stop = 1
+					forvalues k = 1/`=`m'' {
+						qui count if ``j'' == `=`m' - `k'' & `gp' == 0
+						local v`k'1_0 = r(N)
+						qui count if ``j'' == `=`m' - `k'' & `gp' == 1
+						local v`k'1_1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
+						local v`k'2_0 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
+						local v`k'2_1 = r(N)
+						if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0 ) & `stop' != 0 {
+							qui replace ``j''= `=`m' - `k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''= `=`m' - `k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else {														// Moda central non utilisée
+					if runiform()>0.5{											// Tirage au sort pour regrouper
+						local stop = 1
+						forvalues k = 1/`m' {
+							qui count if ``j'' == `=`m' - `k'' & `gp' == 0
+							local v`k'1_0 = r(N)
+							qui count if ``j'' == `=`m' - `k'' & `gp' == 1
+							local v`k'1_1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
+							local v`k'2_0 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
+							local v`k'2_1 = r(N)
+							if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
+								qui replace ``j''= `=`m'-`k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m'-`k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+								local stop = 0
+							}
+						}
+					}
+					else {
+						local stop = 1
+						forvalues k = 1/`=`nbm_`j''-`m'' {
+							qui count if ``j'' == `=`m' + `k'' & `gp' == 0
+							local v`k'1_0 = r(N)
+							qui count if ``j'' == `=`m' + `k'' & `gp' == 1
+							local v`k'1_1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
+							local v`k'2_0 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
+							local v`k'2_1 = r(N)
+							if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0{
+								qui replace ``j''=`=`m' + `k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+								local stop = 0
+							}
+							else {
+								if `stop' != 0 {
+									qui replace ``j''= `nbm_`j'' if ``j''==`m'
+									qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
+									di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
+									local stop = 0
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+
+if `com_z' == 1 {
+	di
+	di "WARNING : Automatic recoding, the first response category is 0. see {help rosali:help rosali}."
+	di
+}
+
+forvalues j =1/`nbitems' {
+		qui tab ``j'', matrow(rec)					// Récupération des infos moda du temps 1
+		local nbm`j'_t1 = r(r)
+
+		qui tab ``=`j'+`nbitems''' 					// Récupération des infos moda du temps 2
+		local nbm`j'_t2 = r(r)
+
+	local nbm_`j' = max(`nbm`j'_t1', `nbm`j'_t2')
+	//Recodage des réponses en 0, 1, 2, etc...
+	forvalues r = 0/`=`nbm_`j''-1'  {
+		qui replace ``j'' = `r' if ``j'' == `=rec[`=`r'+1',1]'
+		qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=rec[`=`r'+1',1]'
+	}
+}
+
+/* Calcul de nbmoda & nbdif */
+forvalues j = 1/`nbitems' {
+	qui tab ``j''
+	local nbmoda_`j' = r(r)
+	local nbdif_`j' = r(r) - 1
+}
+
+local maxdif = 0
+local nbmoda_sum = 0
+forvalues j = 1/`nbitems' {
+	if `maxdif' < `nbdif_`j'' {
+		local maxdif = `nbdif_`j''
+	}
+	local nbmoda_sum = `nbmoda_sum' + `nbdif_`j''
+}
+
+/* Au moins 2 moda par item */
+forvalues j=1/`nbitems' {
+	if `nbmoda_`j'' == 1 {
+	di as error "``j'' have only one response category, the analysis can be performed only if each item has at least 2 response categories"
+		error 198
+		exit
+	}
+}
+
+local coln ""
+forvalues j =1 /`nbitems' {
+	local coln "`coln' ``j''"
+}
+
+matrix nbmod = J(2,`nbitems',.)
+
+matrix colnames nbmod = `coln'
+matrix rownames nbmod = NbModa Recoding
+
+forvalues j = 1/`nbitems' {
+	matrix nbmod[1,`j'] = `nbmoda_`j''
+	matrix nbmod[2,`j'] = `recoda_`j''
+}
+
+*Erreur si plus de 200 difficultés
+local nb_test = 0
+forvalues j=1/`nbitems' {
+	local nb_test = `nb_test'+`nbmoda_`j'' -1
+}
+
+if `nb_test' >= 200 {
+	di as error "The total number of items difficulties to be estimated must be less than 200 ({hi:moda} option option)."
+	error 198
+	exit
+}
+
+local nbitp = 0
+
+forvalues j = 1/`nbitems' {
+	if `nbmoda_`j'' >= 2 {
+		local nbitp = `nbitp' + 1
+	}
+}
+
+qui count
+local nbpat = r(N)
+
+
+/*********************************
+* AFFICHAGE INITIAL
+*********************************/
+di
+di _col(5) "{hline 78}"
+di  _col(15) "Time 1" _col(42) "Time 2" _col(65) "Nb of Answer Cat."
+di _col(5) "{hline 78}"
+forvalues j=1/`nbitems' {
+    di as text _col(15) abbrev("``j''",20)  _col(42) abbrev("``=`j'+`nbitems'''",20) _col(65) `nbmoda_`j''
+}
+di _col(5) "{hline 78}"
+if "`group'" != "" {
+	di _col(10) "Nb of patients: "  abbrev("`gp'",20) " 0 = `nbp_gp0' ;", abbrev("`gp'",20) " 1 = `nbp_gp1'"
+	di _col(5) "{hline 78}"
+}
+else {
+	di _col(10) "Nb. of patients: `nbpat'"
+	di _col(5) "{hline 78}"
+}
+di
+if `nbitems' == 1 {
+	di as error "The analysis can only be performed with at least 2 items."
+	error 198
+	exit
+}
+forvalues j = 1/`nbitems' {
+	if `nbmoda_`j'' == 2 {
+		di "WARNING: ``j'' has only 2 response categories, no distinction can be made between uniform or non-uniform recalibration."
+	}
+	if `nbmoda_`j'' == 1 {
+		di as error "Only `nbmoda_`j'' response categories of item ``j'' were used by the sample, the analysis cannot be performed."
+		error 198
+		exit
+	}
+	if `nbmoda_`j'' == 0 {
+		di as error "No response categories of item ``j'' were used by the sample, the analysis cannot be performed."
+		error 198
+		exit
+	}
+}
+di
+if "`group'" != "" {
+	di _col(2) as text "For all models : - mean of the latent trait in `gp' 0 at time 1 is constrained at 0"
+	di _col(19) "- equality of variances between groups"
+	di
+}
+else {
+	di _col(2) as text "For all models : mean of the latent trait at time 1 is constrained at 0"
+	di
+}
+
+
+
+/*********************************
+* DEFINITION DES CONTRAINTES
+*********************************/
+
+if "`group'"!="" { // Contraintes si option groupe
+	*EGALITE ENTRE GROUPES A T1 (1-200)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=0+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``j'']1.`gp'
+		}
+	}
+
+	*DIF UNIFORME A T1 (201-400)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=200+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp'=`p'*[1.``j'']1.`gp'-`p'*[1.``j'']0bn.`gp'
+		}
+	}
+
+	*EGALITES ENTRE T1 et T2, groupe 0 (401-600)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp'
+		}
+	}
+
+	*EGALITES ENTRE T1 et T2, groupe 1 (601-800)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=600+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'
+		}
+	}
+
+	* RC COMMUNE (801-1000)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=800+`maxdif'*(`j'-1)+`p'' [`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
+		}
+	}
+
+	* RC UNIFORME, groupe 0 (1001-1200)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'
+		}
+	}
+
+	* RC UNIFORME, groupe 1 (1201-1400)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1200+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
+		}
+	}
+
+	*Sans interaction temps x groupe
+	constraint 1999 [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'=[/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
+}
+else { //Contraintes si pas d'option groupe
+	*EGALITE ENTRE T1 et T2 (401-600)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']:_cons = [`p'.``=`j'+`nbitems''']:_cons
+		}
+	}
+	*RC UNIFORME (1001-1200)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']:_cons - [1.``j'']:_cons)=[`p'.``=`j'+`nbitems''']:_cons -[`p'.``j'']:_cons
+		}
+	}
+}
+
+/*********************************
+* MATRICE DES RESULTATS
+*********************************/
+matrix dif_rc=J(`nbitems',8,.)
+matrix colnames dif_rc=DIFT1 DIFU RC RC_DIF RCG0 RCUG0 RCG1 RCUG1
+local rown ""
+
+forvalues j =1 /`nbitems' {
+	local rown "`rown' ``j''"
+}
+matrix rownames dif_rc = `rown'
+
+*Nb modalité max
+local nbdif_max = 0
+forvalues j=1/`nbitems' {
+	if `nbdif_max' < `nbdif_`j'' {
+		local nbdif_max = `nbdif_`j''
+	}
+}
+
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////					PARTIE 1 : DIF A T1 ?						////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+
+if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "nodif"
+
+		di _dup(49) "_ "
+		di
+		di  as input "PART 1: DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS AT TIME 1"
+
+    *********************************
+  	**			MODEL B			   **
+  	*********************************
+
+  	local model ""
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			local model "`model' (`p'.``j''<-THETA@`p')"
+  		}
+  	}
+
+  	qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading cons) var(0: THETA@v) var(1:THETA@v) latent(THETA) nocapslatent
+  	/* Stockage des estimations du modèle */
+  	estimates store modeldifB
+  	matrix val_mB = r(table)
+  	matrix esti_B = e(b)
+
+
+  	/* Calcul des difficultés d'item (delta_j) */
+  	matrix delta_mB=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+  		}
+  	}
+  	local name_partOneL ""
+
+  	forvalues j=1/`nbitems' {
+  			local name_partOneL "`name_partOneL' ``j''"
+  	}
+
+  	matrix colnames delta_mB = `name_partOneC'
+  	matrix rownames delta_mB = `name_partOneL'
+  		matrix delta_mB_se=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC_se ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+  		}
+  	}
+
+  	matrix colnames delta_mB_se = `name_partOneC_se'
+  	matrix rownames delta_mB_se = `name_partOneL'
+
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			forvalues g=0/1{
+  				qui lincom -[`p'.``j'']:`g'.`gp'
+  				local delta`j'_`p'g`g'mB=r(estimate)
+  				local delta`j'_`p'g`g'mB_se=r(se)
+  				if `p'>1{
+  					qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+  					local delta`j'_`p'g`g'mB = r(estimate)
+  					local delta`j'_`p'g`g'mB_se = r(se)
+  				}
+  				matrix delta_mB[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB'
+  				matrix delta_mB_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB_se'
+  			}
+  		}
+  	}
+
+  	matrix var_mB = (val_mB[1,"/var(THETA)#0bn.`gp'"]\val_mB[2,"/var(THETA)#0bn.`gp'"])
+
+  	/*group effect*/
+  	qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
+  	local geffmB=r(estimate)
+  	local segeffmB=r(se)
+  	qui test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
+  	local gcmBp=r(p)
+  	local gcmBchi=r(chi2)
+  	local gcmBdf=r(df)
+
+
+  	*********************************
+  	**			MODEL A			   **
+  	*********************************
+
+  	local model ""
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			local model "`model' (`p'.``j''<-THETA@`p')"
+  		}
+  	}
+
+  	 qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading means) var(0: THETA@v) var(1:THETA@v) from(esti_B, skip) latent(THETA) nocapslatent
+
+  	/* Stockage des estimations du modèle */
+  	estimates store modeldifA
+  	matrix val_mA = r(table)
+  	matrix esti_A = e(b)
+
+  	/* Calcul des difficultés d'item (delta_j) */
+  	matrix delta_mA=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+  		}
+  	}
+  	local name_partOneL ""
+
+  	forvalues j=1/`nbitems' {
+  			local name_partOneL "`name_partOneL' ``j''"
+  	}
+  	matrix colnames delta_mA = `name_partOneC'
+  	matrix rownames delta_mA = `name_partOneL'
+  	matrix delta_mA_se=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC_se ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+  		}
+  	}
+
+  	matrix colnames delta_mA_se = `name_partOneC_se'
+  	matrix rownames delta_mA_se = `name_partOneL'
+
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			forvalues g=0/1{
+  				qui lincom -[`p'.``j'']:`g'.`gp'
+  				local delta`j'_`p'g`g'mA=r(estimate)
+  				local delta`j'_`p'g`g'mA_se=r(se)
+  				if `p'>1{
+  					qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+  					local delta`j'_`p'g`g'mA = r(estimate)
+  					local delta`j'_`p'g`g'mA_se = r(se)
+  				}
+  				matrix delta_mA[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA'
+  				matrix delta_mA_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA_se'
+  			}
+  		}
+  	}
+  	//Variance et se mA
+  	matrix var_mA = (val_mA[1,"/var(THETA)#0bn.`gp'"]\val_mA[2,"/var(THETA)#0bn.`gp'"])
+
+	*********************************
+	*************MODEL C*************
+	*********************************
+	// Etape itérative si lrtest significatif
+	local nb_stepC = 0
+  local diftestp = 1
+	if `diftestp'<2{  /*If pvalue(LRtest)<0.05 then step C*/
+		di
+		di as input "PROCESSING STEP C"
+		di
+
+		/*test DIF pour chaque item*/
+		local boucle = 1
+		local stop = 0
+		while `boucle'<=`=`nbitp'-1' & `stop'==0{ /*on s'arrête quand on a libéré du DIF sur (tous les items-1) ou lorsqu'il n'y a plus de tests significatifs*/
+			local nb_stepC = `boucle'
+			local pajust=0.05
+			/*réinitialisation de la matrice de test*/
+			matrix test_difu_`boucle'=J(`nbitems',3,.)
+			matrix colnames test_difu_`boucle'=chi_DIFU df_DIFU pvalueDIFU
+			matrix test_dif_`boucle'=J(`nbitems',3,.)
+			matrix colnames test_dif_`boucle'=chi_DIF df_DIF pvalueDIF
+			local nbsig=0
+			local minpval=1
+			local itemdif=0
+			if "`detail'" != ""{
+
+				di as text "Loop `boucle'"
+				di as text _col(5) "Adjusted  alpha: " %6.4f  `pajust'
+				di
+				di as text _col(10) "{hline 65}"
+				di as text _col(10) "Freed item" _col(31) "Chi-Square"  _col(48) "DF" _col(57) "P-Value"
+				di as text _col(10) "{hline 65}"
+			}
+			/*boucle de test*/
+			forvalues j=1/`nbitems'{
+				//if `nbdif_`j'' > 2 {
+					local model ""
+					local listconst ""
+					if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF déjà détecté sur l'item j*/
+						/*on libère le DIF de l'item i: pas de contraintes*/
+						forvalues k=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes*/
+							if `k'!=`j' & `nbmoda_`j'' >= 2 {
+								if dif_rc[`k',1]==. | dif_rc[`k',1]==0 {/*pas de DIF sur item k: contraintes 1-200*/
+									forvalues p=1/`nbdif_`k''{
+										qui local listconst "`listconst' `=0+`maxdif'*(`k'-1)+`p''"
+										qui constraint list `=0+`maxdif'*(`k'-1)+`p''
+									}
+								}
+								else{
+									if dif_rc[`k',2]!=. & dif_rc[`k',2]!= 0 & `nbmoda_`k'' > 2 { /*DIF U: contraintes 201-400*/
+										forvalues p=2/`nbdif_`k''{
+											qui local listconst "`listconst' `=200+`maxdif'*(`k'-1)+`p''"
+											qui constraint list `=200+`maxdif'*(`k'-1)+`p''
+										}
+									}
+								}
+							}
+						}
+						forvalues jj=1/`nbitems'{
+							forvalues p=1/`nbdif_`jj''{
+								local model "`model' (`p'.``jj''<-THETA@`p')"
+							}
+						}
+						qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
+						estimates store modeldif3b`boucle'it`i'
+
+						*************************
+						*****test DIF item i*****
+						*************************
+						qui test [1.``j'']1.`gp'=[1.``j'']0bn.`gp'
+						if `nbmoda_`j'' > 2 {
+							forvalues p=2/`nbdif_`j''{
+								qui test [`p'.``j'']1.`gp'=[`p'.``j'']0bn.`gp', acc
+							}
+						}
+						matrix test_dif_`boucle'[`j',1]=(r(chi2),r(df),r(p))
+
+						/* Test DIF Uniforme */
+						if `nbmoda_`j'' > 2 {
+							qui test 2*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[2.``j'']1.`gp'-[2.``j'']0bn.`gp'
+							forvalues p=3/`nbdif_`j''{
+								qui test `p'*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp', acc
+							}
+							matrix test_difu_`boucle'[`j',1]=(r(chi2), r(df), r(p))
+						}
+
+						if test_dif_`boucle'[`j',3]<`pajust'{/*si DIF sur item i*/
+							local ++nbsig
+							if test_dif_`boucle'[`j',3]<`minpval'{
+								local minpval=test_dif_`boucle'[`j',3]
+								local itemdif=`j'
+							}
+						}
+						if "`detail'" != "" {
+							di as text  _col(10) abbrev("``j''",15)  as result _col(31) %6.3f test_dif_`boucle'[`j',1] _col(48) test_dif_`boucle'[`j',2] _col(57)  %6.4f test_dif_`boucle'[`j',3]
+						}
+					}
+			}
+			/*si nb de tests significatifs=0, on arrête*/
+			if `nbsig'==0{
+				local stop=1
+				if `boucle' == 1 {
+					if "`detail'" != "" {
+						di as text _col(10) "{hline 65}"
+						di
+						di as result  "No significant test: no difference between groups detected, no DIF detected"
+						di
+					}
+				}
+				else {
+					if "`detail'" != ""{
+						di as text _col(10) "{hline 65}"
+						di
+						di as result "No other significant tests"
+						di
+					}
+				}
+			}
+			else{/*si nb de tests significatifs>0, mise à jour de la matrice de résultats*/
+				matrix dif_rc[`itemdif',1]=`boucle'
+				if "`detail'" != ""{
+					di as text _col(10) "{hline 65}"
+					di
+					di as result "Difference between groups on ``itemdif'' at time 1"
+				}
+				if `nbmoda_`itemdif'' > 2 {
+					if "`detail'" != "" {
+
+						di
+						di %~60s as text "Test of uniform difference"
+						di _col(10) "{hline 40}"
+						di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
+						di _col(10) as result %4.2f `=test_difu_`boucle'[`itemdif',1]' _col(28) `=test_difu_`boucle'[`itemdif',2]' _col(40) %4.2f `=test_difu_`boucle'[`itemdif',3]'
+						di _col(10) as text "{hline 40}"
+					}
+					if test_difu_`boucle'[`itemdif',3]<0.05{	/*DIF NU détectée*/
+						matrix dif_rc[`itemdif',2]=0
+							di
+							di as result "``itemdif'' : Non-uniform differences of item difficulties between groups at T1"
+							di
+					}
+					else{/*DIF U détectée*/
+						matrix dif_rc[`itemdif',2]=`boucle'
+							di
+							di as result "``itemdif'' : Uniform differences of item difficulties between groups at T1"
+							di
+					}
+				}
+				else {
+					// Différence entre groupes au temps 1 mais slmt 2 moda. donc pas de U ou NU
+					di _col(15) _dup(60) "-"
+				}
+			}
+			local ++boucle
+		}
+	}
+
+	/* MODELE FINAL DE LA PARTIE 1. Si DIFT1 détecté (=Au moins 2 boucles dans l'étape C)*/
+	if `nb_stepC' > 1 {
+		forvalues j=1/`nbitems'{
+			local model ""
+			local listconst ""
+			if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF: contraintes 1-200*/
+				forvalues p=1/`nbdif_`j''{
+					qui local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
+					qui constraint list `=0+`maxdif'*(`j'-1)+`p''
+				}
+			}
+			else {
+				if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { /*DIF U: contraintes 201-400*/
+					forvalues p=2/`nbdif_`j''{
+						qui local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
+						qui constraint list `=200+`maxdif'*(`j'-1)+`p''
+					}
+				}
+			}
+		}
+		forvalues j=1/`nbitems'{
+			forvalues p=1/`nbdif_`j''{
+				local model "`model' (`p'.``j''<-THETA@`p')"
+			}
+		}
+
+		qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
+		/* Stockage des estimations du modèle */
+		estimates store modeldifCFin
+		matrix val_mC = r(table)
+
+		/* Calcul des difficultés d'item (delta_j) */
+		matrix delta_mCFin=J(`nbitems',`=`nbdif_max'*2',.)
+		local name_partOneC ""
+		forvalues p=1/`nbdif_max' {
+			forvalues g=0/1 {
+				local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+			}
+		}
+		local name_partOneL ""
+		forvalues j=1/`nbitems' {
+			local name_partOneL "`name_partOneL' ``j''"
+		}
+		matrix colnames delta_mCFin = `name_partOneC'
+		matrix rownames delta_mCFin = `name_partOneL'
+
+		matrix delta_mCFin_se=J(`nbitems',`=`nbdif_max'*2',.)
+		local name_partOneC_se ""
+
+		forvalues p=1/`nbdif_max' {
+			forvalues g=0/1 {
+				local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+			}
+		}
+		matrix colnames delta_mCFin_se = `name_partOneC_se'
+		matrix rownames delta_mCFin_se = `name_partOneL'
+
+		forvalues j=1/`nbitems'{
+			forvalues p=1/`nbdif_`j''{
+				forvalues g=0/1{
+					qui lincom -[`p'.``j'']:`g'.`gp'
+					local delta`j'_`p'g`g'mCFin=r(estimate)
+					local delta`j'_`p'g`g'mCFin_se=r(se)
+					if `p'>1{
+						qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+						local delta`j'_`p'g`g'mCFin = r(estimate)
+						local delta`j'_`p'g`g'mCFin_se = r(se)
+					}
+					matrix delta_mCFin[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin'
+					matrix delta_mCFin_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin_se'
+				}
+			}
+		}
+		if "`group'" != "" { //Variance et se mA
+			matrix var_mC = (val_mC[1,"/var(THETA)#0bn.`gp'"]\val_mC[2,"/var(THETA)#0bn.`gp'"])
+		}
+			/*group effect*/
+		qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
+		local geffmCFin=r(estimate)
+		local segeffmCFin=r(se)
+		qui	test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
+		local gcmCFinp=r(p)
+		local gcmCFinchi=r(chi2)
+		local gcmCFindf=r(df)
+	}
+}
+
+	*********************************
+	 ***		BILAN  			***
+	*********************************
+
+	if "`group'" != "" & "`nodif'" == "" {
+	di
+	di %~84s as result "SUMMARY"
+	di as result _col(2)  "{hline 80}"
+	di as result 		 _col(18) "Difference in"
+	di as result _col(2) "Item" _col(18) "groups at T1" _col(36) "Recalibration"  _col(54) "RC " abbrev("`gp'",10) " 0"  _col(72) "RC " abbrev("`gp'",10) " 1"
+	di as result _col(2) "{hline 80}"
+	forvalues j=1/`nbitems' {
+		local RC
+		local RCg0
+		local RCg1
+		local difft1
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
+			local RC "Common"
+		}
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
+			local RC "Differential"
+		}
+		if `nbmoda_`j'' > 2 {
+			if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+				local RCg0 "Uniform"
+			}
+			if (dif_rc[`j',6] == 0) {
+				local RCg0 "Non-uniform"
+			}
+				if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
+				local RCg1 "Uniform"
+			}
+			if ( dif_rc[`j',8] == 0) {
+				local RCg1 "Non-uniform"
+			}
+			if (dif_rc[`j',1] != . ) {
+				if (dif_rc[`j',2]!=0) {
+					local difft1 "Uniform"
+				}
+				else {
+					local difft1 "Non-uniform"
+				}
+			}
+		}
+		else {
+			if dif_rc[`j',6] != . {
+				local RCg0 " X "
+			}
+			if dif_rc[`j',8] != . {
+				local RCg1 " X "
+			}
+			if dif_rc[`j',1] != . {
+				local difft1 " X "
+			}
+		}
+		di as result _col(2) abbrev("``j''",15) as text _col(18) "`difft1'"  _col(36) "`RC'"  _col(54) "`RCg0'"  _col(72) "`RCg1'"
+	}
+	di as result _col(2) "{hline 80}"
+	di
+}
+else if "`group'" != "" & "`nodif'" != "" {
+	di
+	di %~90s as result "SUMMARY"
+	di as result _col(10)  "{hline 70}"
+	di as result _col(10) "Item" _col(26) "Recalibration"  _col(46) "RC `gp' 0"  _col(62) "RC `gp' 1"
+	di _col(10) "{hline 70}"
+	forvalues j=1/`nbitems' {
+		local RC
+		local RCg0
+		local RCg1
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
+			local RC "Common"
+		}
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
+			local RC "Differential"
+		}
+		if `nbmoda_`j'' > 2 {
+			if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+				local RCg0 "Uniform"
+			}
+			if (dif_rc[`j',6] == 0) {
+				local RCg0 "Non-uniform"
+			}
+			if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
+				local RCg1 "Uniform"
+			}
+			if ( dif_rc[`j',8] == 0) {
+				local RCg1 "Non-uniform"
+			}
+		}
+		else {
+			if dif_rc[`j',6] != . {
+				local RCg0 " X "
+			}
+			if dif_rc[`j',8] != . {
+				local RCg1 " X "
+			}
+		}
+		di as result _col(10) "``j''" as text  _col(26) "`RC'"  _col(44) "`RCg0'"  _col(62) "`RCg1'"
+	}
+	di as result _col(10) "{hline 70}"
+}
+else if "`group'" == "" {
+	di
+	di %~60s as result "SUMMARY"
+	di as result _col(10)  "{hline 40}"
+	di _col(10) "Item" _col(36) "Recalibration"
+	di _col(10) "{hline 40}"
+	forvalues j=1/`nbitems' {
+		local RC
+		if dif_rc[`j',3] != . {
+			if `nbmoda_`j'' > 2 {
+				if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+					local RC "Uniform"
+				}
+				if (dif_rc[`j',6] == 0) {
+					local RC "Non-uniform"
+				}
+			}
+			else {
+				local RC " X "
+			}
+		}
+		di as result _col(10) "``j''" as text  _col(38) "`RC'"
+	}
+	di as result _col(10) "{hline 40}"
+	di
+}
+
+matrix dif_detect = J(1,`nbitems',.)
+local numdif=1
+forvalues j=1/`nbitems' {
+  if dif_rc[`j',1] != . {
+    matrix dif_detect[1,`numdif']=`j'
+    local numdif = `numdif'+1
+  }
+}
+return matrix difitems = dif_detect
+
+capture qui use `saverspcm', clear
+
+end
diff --git a/Modules/rosali_custom/rosali_original.ado b/Modules/rosali_custom/rosali_original.ado
new file mode 100644
index 0000000..7aa7b91
--- /dev/null
+++ b/Modules/rosali_custom/rosali_original.ado
@@ -0,0 +1,1150 @@
+*! version 2.4 june2020
+*! Myriam Blanchin - Priscilla Brisson
+************************************************************************************************************
+* ROSALI: RespOnse-Shift ALgorithm at Item-level
+* Response-shift detection based on Rasch models family
+*
+* Version 1 : December 21, 2016 (Myriam Blanchin) /*rspcm122016*/
+* Version 1.1 : October 13, 2017 (Myriam Blanchin) /*option: MODA, automatic recoding of unused response categories*/
+* Version 2 : April, 2018 (Myriam Blanchin - Priscilla Brisson) /*option: GROUP, dichotomous group variable*/
+* Version 2.1 : October, 2018 (Myriam Blanchin - Priscilla Brisson) /* Version 1.1 + Version 2 */
+* Version 2.2 : February, 2019 (Priscilla Brisson) /* option nodif, optimization */
+* Version 2.3 : December, 2019 (Priscilla Brisson) /* option detail, + petites corrections */
+* Version 2.4 : June, 2020 (Myriam Blanchin) /* debug option detail + step C, modifs sorties et help */
+*
+* Myriam Blanchin, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* myriam.blanchin@univ-nantes.fr
+*
+* Priscilla Brisson, SPHERE, Faculty of Pharmaceutical Sciences - University of Nantes - France
+* priscilla.brisson@univ-nantes.fr
+*
+* This program is free software; you can redistribute it and/or modify
+* it under the terms of the GNU General Public License as published by
+* the Free Software Foundation; either version 2 of the License, or
+* (at your option) any later version.
+*
+* This program is distributed in the hope that it will be useful,
+* but WITHOUT ANY WARRANTY; without even the implied warranty of
+* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+* GNU General Public License for more details.
+*
+* You should have received a copy of the GNU General Public License
+* along with this program; if not, write to the Free Software
+* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+************************************************************************************************************/
+
+program define rosali_original, rclass
+
+timer clear 1
+timer on 1
+
+syntax varlist(min=2 numeric) [if] [,GROUP(varlist) NODIF PRO DETail]
+
+preserve
+version 15
+tempfile saverspcm
+capture qui save `saverspcm',replace
+local save1=_rc
+
+if "`if'"!="" {
+    qui keep `if'
+}
+
+if "`pro'" != "" {
+	di "START"
+}
+
+/**************************************************************************/
+set more off
+set matsize 5000
+constraint drop _all
+
+local gp "`group'"
+
+tokenize `varlist'
+local nbitems:word count `varlist'
+
+	/* Vérif nb d'items pair */
+local mod=mod(`nbitems',2)
+if `mod'!=0 {
+  di as error "You must enter an even number of items : the first half of the items represents the items at time 1 and the second half the items at time 2"
+  error 198
+  exit
+}
+
+local nbitems=`nbitems'/2
+
+
+if "`group'"=="" & "`nodif'"!="" {
+	di as error "nodif can only be used with the group option ({hi:nodif} option). Please correct this option."
+	error 198
+	exit
+}
+
+local nbc: word count `group'
+if `nbc' >= 2 {
+	di as error "Only one variable can be used for group option ({hi:group} option). Please correct this option."
+	error 198
+	exit
+}
+
+	/* Vérif qu'il y a 2 groupes si l'option groupe est choisie */
+if "`group'"!="" {
+  qui tab `group'
+  local nbgrp = r(r)
+  if `nbgrp' != 2 {
+	di as error "Only 2 groups are possible for the group option ({hi:group} option). Please correct this option."
+	error 420
+	exit
+  }
+}
+/* recoder la variable de groupe en 0, 1*/
+
+if "`group'"!="" {
+	qui tab `gp', matrow(rep)
+	qui matrix list rep
+	if rep[1,1]+rep[2,1] != 1 & rep[1,1]*rep[2,1] != 0 {
+		forvalues i=1/`=rowsof(rep)'{
+			qui replace `gp'=`i'-1 if `gp'==rep[`i',1]
+			di "WARNING : `gp' `=rep[`i',1]' is now `gp' `=`i'-1' "
+		}
+	}
+	forvalues g = 0/1 {
+		qui tab `gp' if `gp' == `g'
+		local nbp_gp`g' = r(N)
+	}
+}
+
+
+
+/*item rename*/
+/*
+Items au temps 1 : 1 à nbitems ``j''
+Items au temps 2 : nbitems à 2*nbitems ``=`j'+`nbitems'''
+
+Si t varie, puis num item : ``=(`t'-1)*`nbitems'+`j'''
+*/
+
+
+local com_z = 0 // Indicatrice de recodage
+	/*verif modalités répondues*/
+if "`gp'" == "" {							// Si pas d'option groupe
+	forvalues j = 1 / `nbitems' {
+		local recoda_`j' = 0
+		qui tab ``j'', matrow(rect1_`j')				// Récupération des infos moda du temps 1
+		local minm`j'_t1 = rect1_`j'[1,1]
+		local maxm`j'_t1 = rect1_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''', matrow(rect2_`j')			// Récupération des infos moda du temps 2
+		local minm`j'_t2 = rect2_`j'[1,1]
+		local maxm`j'_t2 = rect2_`j'[r(r),1]
+
+		local minm_`j' = min(`minm`j'_t1',`minm`j'_t2')		// Info moda pour l'item j
+		local maxm_`j' = max(`maxm`j'_t1',`maxm`j'_t2')
+		local nbm_`j' = `=`maxm_`j''-`minm_`j'''
+
+		if `minm_`j'' != 0 & `com_z' == 0 {
+			local com_z = 1
+		}
+
+
+		//Recodage des réponses en 0, 1, 2, etc...
+		forvalues r = 0/`=`maxm_`j''-1'  {
+				qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
+				qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
+		}
+
+		// Vérif. Que toutes les modas sont utilisées & concordance entre temps
+		forvalues m = 0/`nbm_`j'' {
+			qui count if ``j'' == `m'
+			local nb_rn1 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m'
+			local nb_rn2 = r(N)
+			local nb_rn = min(`nb_rn1',`nb_rn2')
+
+			if `nb_rn' == 0 {		// Une moda n'est pas utilisée
+				local recoda_`j' = 1
+				if   `m' == 0 | `m' <= `minm`j'_t1' | `m' <= `minm`j'_t2' { // La moda 0 ou les moda min ne sont pas utilisées
+					local stop = 1
+					forvalues k = 1/`=`nbm_`j''-`m'' {
+						qui count if ``j'' == `=`m' + `k''
+						local v`k'1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k''
+						local v`k'2 = r(N)
+						if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+							qui replace ``j''= `=`m'+`k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged "
+							local stop = 0
+						}
+					}
+				}
+				else if `m' >= `maxm`j'_t1' | `m' >= `maxm`j'_t2' | `m' == `maxm_`j'' {	// La (ou les) moda max ne sont pas utilisée(s)
+					local stop = 1
+					forvalues k = 1/`m' {
+						qui count if ``j'' == `=`m' - `k''
+						local v`k'1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k''
+						local v`k'2 = r(N)
+						if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+							qui replace ``j''=`=`m' - `k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m' - `k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else {
+					if runiform()>0.5{											// Tirage au sort pour regrouper
+						local stop = 1
+						forvalues k = 1/`m' {
+							qui count if ``j'' == `=`m' - `k''
+							local v`k'1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k''
+							local v`k'2 = r(N)
+							if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+								qui replace ``j''= `=`m'-`k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems''' =`=`m'-`k'' if ``=`j'+`nbitems''' ==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+								local stop = 0
+							}
+						}
+					}
+					else {
+						local stop = 1
+						forvalues k = 1/`=`nbm_`j''-`m'' {
+							qui count if ``j'' == `=`m' + `k''
+							local v`k'1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k''
+							local v`k'2 = r(N)
+							if (`v`k'1' != 0 | `v`k'2' != 0) & `stop' != 0 {
+								qui replace ``j''=`=`m' + `k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+								local stop = 0
+							}
+							else {
+								if `stop' != 0 {
+									qui replace ``j''= `nbm_`j'' if ``j''==`m'
+									qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
+									di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
+									local stop = 0
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+else {									// Cas où l'option groupe est utilisée
+	forvalues j = 1 / `nbitems' {
+		local recoda_`j' = 0
+		qui tab ``j'' if `gp' == 0, matrow(rect1_g0_`j') matcell(nbrt1_g0_`j')					// Récupération des infos moda du temps 1pour chaque groupe
+		local minm`j'_t1_g0 = rect1_g0_`j'[1,1]
+		local maxm`j'_t1_g0 = rect1_g0_`j'[r(r),1]
+
+		qui tab ``j'' if `gp' == 1, matrow(rect1_g1_`j') matcell(nbrt1_g1_`j')
+		local minm`j'_t1_g1 = rect1_g1_`j'[1,1]
+		local maxm`j'_t1_g1 = rect1_g1_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''' if `gp' == 0, matrow(rect2_g0_`j') matcell(nbrt2_g0_`j')				// Récupération des infos moda du temps 2 pour chaque groupe
+		local minm`j'_t2_g0 = rect2_g0_`j'[1,1]
+		local maxm`j'_t2_g0 = rect2_g0_`j'[r(r),1]
+
+		qui tab ``=`j'+`nbitems''' if `gp' == 1	, matrow(rect2_g1_`j') matcell(nbrt2_g1_`j')
+		local minm`j'_t2_g1 = rect2_g0_`j'[1,1]
+		local maxm`j'_t2_g1 = rect2_g0_`j'[r(r),1]
+
+		local minm_`j' = min(`minm`j'_t1_g0',`minm`j'_t2_g0',`minm`j'_t1_g1',`minm`j'_t2_g1')		// Info moda pour l'item j
+		local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t2_g0',`maxm`j'_t1_g1',`maxm`j'_t2_g1')
+		local nbm_`j' = `=`maxm_`j''-`minm_`j''+1'
+
+		if `minm_`j'' != 0 & `com_z' == 0 {
+			local com_z = 1
+		}
+		//Recodage des réponses en 0, 1, 2, etc...
+		forvalues r = 0/`=`maxm_`j''-1'  {
+			qui replace ``j'' = `r' if ``j'' == `=`r'+`minm_`j'''
+			qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=`r'+`minm_`j'''
+		}
+
+		// Vérif. Que toutes les modas sont utilisées & concordance entre temps
+		forvalues m = 0/`=`nbm_`j''-1' {
+			qui count if ``j'' == `m' & `gp' == 0
+			local nb_rn1_g0 = r(N)
+			qui count if ``j'' == `m' & `gp' == 1
+			local nb_rn1_g1 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m' & `gp' == 0
+			local nb_rn2_g0 = r(N)
+			qui count if ``=`j'+`nbitems''' == `m' & `gp' == 1
+			local nb_rn2_g1 = r(N)
+			local nb_rn = min(`nb_rn1_g0',`nb_rn2_g0',`nb_rn1_g1',`nb_rn2_g1')
+
+			if `nb_rn' == 0 {		// Une moda n'est pas utilisée
+				local recoda_`j' = 1
+				if  `m' == 0 | `m' < `minm`j'_t1_g0' | `m' < `minm`j'_t2_g0' | `m' < `minm`j'_t1_g1' | `m' < `minm`j'_t2_g1' { // La moda 0 n'est pas utilisée
+					local stop = 1
+					forvalues k = 1/`=`nbm_`j''-`m'' {
+						qui count if ``j'' == `=`m' + `k'' & `gp' == 0
+						local v`k'1_0 = r(N)
+						qui count if ``j'' == `=`m' + `k'' & `gp' == 1
+						local v`k'1_1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
+						local v`k'2_0 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
+						local v`k'2_1 = r(N)
+						if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
+							qui replace ``j''= `=`m'+`k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''=`=`m'+`k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else if `m' == `=`nbm_`j''-1' | `m' >= `maxm`j'_t2_g0' | `m' >= `maxm`j'_t1_g1' | `m' >= `maxm`j'_t2_g1' {	// La moda max n'est pas utilisée
+					local stop = 1
+					forvalues k = 1/`=`m'' {
+						qui count if ``j'' == `=`m' - `k'' & `gp' == 0
+						local v`k'1_0 = r(N)
+						qui count if ``j'' == `=`m' - `k'' & `gp' == 1
+						local v`k'1_1 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
+						local v`k'2_0 = r(N)
+						qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
+						local v`k'2_1 = r(N)
+						if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0 ) & `stop' != 0 {
+							qui replace ``j''= `=`m' - `k'' if ``j''==`m'
+							qui replace ``=`j'+`nbitems'''= `=`m' - `k'' if ``=`j'+`nbitems'''==`m'
+							di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+							local stop = 0
+						}
+					}
+				}
+				else {														// Moda central non utilisée
+					if runiform()>0.5{											// Tirage au sort pour regrouper
+						local stop = 1
+						forvalues k = 1/`m' {
+							qui count if ``j'' == `=`m' - `k'' & `gp' == 0
+							local v`k'1_0 = r(N)
+							qui count if ``j'' == `=`m' - `k'' & `gp' == 1
+							local v`k'1_1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 0
+							local v`k'2_0 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' - `k'' & `gp' == 1
+							local v`k'2_1 = r(N)
+							if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0 {
+								qui replace ``j''= `=`m'-`k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m'-`k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'-`k'' merged"
+								local stop = 0
+							}
+						}
+					}
+					else {
+						local stop = 1
+						forvalues k = 1/`=`nbm_`j''-`m'' {
+							qui count if ``j'' == `=`m' + `k'' & `gp' == 0
+							local v`k'1_0 = r(N)
+							qui count if ``j'' == `=`m' + `k'' & `gp' == 1
+							local v`k'1_1 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 0
+							local v`k'2_0 = r(N)
+							qui count if ``=`j'+`nbitems''' == `=`m' + `k'' & `gp' == 1
+							local v`k'2_1 = r(N)
+							if (`v`k'1_0' != 0 | `v`k'2_0' != 0 | `v`k'1_1' != 0 | `v`k'2_1' != 0) & `stop' != 0{
+								qui replace ``j''=`=`m' + `k'' if ``j''==`m'
+								qui replace ``=`j'+`nbitems'''=`=`m' + `k'' if ``=`j'+`nbitems'''==`m'
+								di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `=`m'+`k'' merged"
+								local stop = 0
+							}
+							else {
+								if `stop' != 0 {
+									qui replace ``j''= `nbm_`j'' if ``j''==`m'
+									qui replace ``=`j'+`nbitems'''= `nbm_`j'' if ``=`j'+`nbitems'''==`m'
+									di  "WARNING: items ``j'' & ``=`j'+`nbitems''': answers `m' and `nbm_`j'' merged"
+									local stop = 0
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}
+
+if `com_z' == 1 {
+	di
+	di "WARNING : Automatic recoding, the first response category is 0. see {help rosali:help rosali}."
+	di
+}
+
+forvalues j =1/`nbitems' {
+		qui tab ``j'', matrow(rec)					// Récupération des infos moda du temps 1
+		local nbm`j'_t1 = r(r)
+
+		qui tab ``=`j'+`nbitems''' 					// Récupération des infos moda du temps 2
+		local nbm`j'_t2 = r(r)
+
+	local nbm_`j' = max(`nbm`j'_t1', `nbm`j'_t2')
+	//Recodage des réponses en 0, 1, 2, etc...
+	forvalues r = 0/`=`nbm_`j''-1'  {
+		qui replace ``j'' = `r' if ``j'' == `=rec[`=`r'+1',1]'
+		qui replace ``=`j'+`nbitems''' = `r' if ``=`j'+`nbitems''' == `=rec[`=`r'+1',1]'
+	}
+}
+
+/* Calcul de nbmoda & nbdif */
+forvalues j = 1/`nbitems' {
+	qui tab ``j''
+	local nbmoda_`j' = r(r)
+	local nbdif_`j' = r(r) - 1
+}
+
+local maxdif = 0
+local nbmoda_sum = 0
+forvalues j = 1/`nbitems' {
+	if `maxdif' < `nbdif_`j'' {
+		local maxdif = `nbdif_`j''
+	}
+	local nbmoda_sum = `nbmoda_sum' + `nbdif_`j''
+}
+
+/* Au moins 2 moda par item */
+forvalues j=1/`nbitems' {
+	if `nbmoda_`j'' == 1 {
+	di as error "``j'' have only one response category, the analysis can be performed only if each item has at least 2 response categories"
+		error 198
+		exit
+	}
+}
+
+local coln ""
+forvalues j =1 /`nbitems' {
+	local coln "`coln' ``j''"
+}
+
+matrix nbmod = J(2,`nbitems',.)
+
+matrix colnames nbmod = `coln'
+matrix rownames nbmod = NbModa Recoding
+
+forvalues j = 1/`nbitems' {
+	matrix nbmod[1,`j'] = `nbmoda_`j''
+	matrix nbmod[2,`j'] = `recoda_`j''
+}
+
+*Erreur si plus de 200 difficultés
+local nb_test = 0
+forvalues j=1/`nbitems' {
+	local nb_test = `nb_test'+`nbmoda_`j'' -1
+}
+
+if `nb_test' >= 200 {
+	di as error "The total number of items difficulties to be estimated must be less than 200 ({hi:moda} option option)."
+	error 198
+	exit
+}
+
+local nbitp = 0
+
+forvalues j = 1/`nbitems' {
+	if `nbmoda_`j'' >= 2 {
+		local nbitp = `nbitp' + 1
+	}
+}
+
+qui count
+local nbpat = r(N)
+
+
+/*********************************
+* AFFICHAGE INITIAL
+*********************************/
+di
+di _col(5) "{hline 78}"
+di  _col(15) "Time 1" _col(42) "Time 2" _col(65) "Nb of Answer Cat."
+di _col(5) "{hline 78}"
+forvalues j=1/`nbitems' {
+    di as text _col(15) abbrev("``j''",20)  _col(42) abbrev("``=`j'+`nbitems'''",20) _col(65) `nbmoda_`j''
+}
+di _col(5) "{hline 78}"
+if "`group'" != "" {
+	di _col(10) "Nb of patients: "  abbrev("`gp'",20) " 0 = `nbp_gp0' ;", abbrev("`gp'",20) " 1 = `nbp_gp1'"
+	di _col(5) "{hline 78}"
+}
+else {
+	di _col(10) "Nb. of patients: `nbpat'"
+	di _col(5) "{hline 78}"
+}
+di
+if `nbitems' == 1 {
+	di as error "The analysis can only be performed with at least 2 items."
+	error 198
+	exit
+}
+forvalues j = 1/`nbitems' {
+	if `nbmoda_`j'' == 2 {
+		di "WARNING: ``j'' has only 2 response categories, no distinction can be made between uniform or non-uniform recalibration."
+	}
+	if `nbmoda_`j'' == 1 {
+		di as error "Only `nbmoda_`j'' response categories of item ``j'' were used by the sample, the analysis cannot be performed."
+		error 198
+		exit
+	}
+	if `nbmoda_`j'' == 0 {
+		di as error "No response categories of item ``j'' were used by the sample, the analysis cannot be performed."
+		error 198
+		exit
+	}
+}
+di
+if "`group'" != "" {
+	di _col(2) as text "For all models : - mean of the latent trait in `gp' 0 at time 1 is constrained at 0"
+	di _col(19) "- equality of variances between groups"
+	di
+}
+else {
+	di _col(2) as text "For all models : mean of the latent trait at time 1 is constrained at 0"
+	di
+}
+
+
+
+/*********************************
+* DEFINITION DES CONTRAINTES
+*********************************/
+
+if "`group'"!="" { // Contraintes si option groupe
+	*EGALITE ENTRE GROUPES A T1 (1-200)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=0+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``j'']1.`gp'
+		}
+	}
+
+	*DIF UNIFORME A T1 (201-400)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=200+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp'=`p'*[1.``j'']1.`gp'-`p'*[1.``j'']0bn.`gp'
+		}
+	}
+
+	*EGALITES ENTRE T1 et T2, groupe 0 (401-600)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']0bn.`gp'
+		}
+	}
+
+	*EGALITES ENTRE T1 et T2, groupe 1 (601-800)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=600+`maxdif'*(`j'-1)+`p'' [`p'.``j'']1.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'
+		}
+	}
+
+	* RC COMMUNE (801-1000)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=800+`maxdif'*(`j'-1)+`p'' [`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
+		}
+	}
+
+	* RC UNIFORME, groupe 0 (1001-1200)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']0bn.`gp'-[1.``j'']0bn.`gp')=[`p'.``=`j'+`nbitems''']0bn.`gp'-[`p'.``j'']0bn.`gp'
+		}
+	}
+
+	* RC UNIFORME, groupe 1 (1201-1400)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1200+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']1.`gp'-[1.``j'']1.`gp')=[`p'.``=`j'+`nbitems''']1.`gp'-[`p'.``j'']1.`gp'
+		}
+	}
+
+	*Sans interaction temps x groupe
+	constraint 1999 [/]:mean(THETA2)#1.`gp'-[/]:mean(THETA2)#0bn.`gp'=[/]:mean(THETA1)#1.`gp'-[/]:mean(THETA1)#0bn.`gp'
+}
+else { //Contraintes si pas d'option groupe
+	*EGALITE ENTRE T1 et T2 (401-600)
+	forvalues j=1/`nbitems'{
+		forvalues p=1/`nbdif_`j''{
+			constraint `=400+`maxdif'*(`j'-1)+`p'' [`p'.``j'']:_cons = [`p'.``=`j'+`nbitems''']:_cons
+		}
+	}
+	*RC UNIFORME (1001-1200)
+	forvalues j=1/`nbitems'{
+		forvalues p=2/`nbdif_`j''{
+			constraint `=1000+`maxdif'*(`j'-1)+`p'' `p'*([1.``=`j'+`nbitems''']:_cons - [1.``j'']:_cons)=[`p'.``=`j'+`nbitems''']:_cons -[`p'.``j'']:_cons
+		}
+	}
+}
+
+/*********************************
+* MATRICE DES RESULTATS
+*********************************/
+matrix dif_rc=J(`nbitems',8,.)
+matrix colnames dif_rc=DIFT1 DIFU RC RC_DIF RCG0 RCUG0 RCG1 RCUG1
+local rown ""
+
+forvalues j =1 /`nbitems' {
+	local rown "`rown' ``j''"
+}
+matrix rownames dif_rc = `rown'
+
+*Nb modalité max
+local nbdif_max = 0
+forvalues j=1/`nbitems' {
+	if `nbdif_max' < `nbdif_`j'' {
+		local nbdif_max = `nbdif_`j''
+	}
+}
+
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////					PARTIE 1 : DIF A T1 ?						////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+
+if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "nodif"
+
+		di _dup(49) "_ "
+		di
+		di  as input "PART 1: DETECTION OF DIFFERENCE IN ITEM DIFFICULTIES BETWEEN GROUPS AT TIME 1"
+
+    *********************************
+  	**			MODEL B			   **
+  	*********************************
+
+  	local model ""
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			local model "`model' (`p'.``j''<-THETA@`p')"
+  		}
+  	}
+
+  	qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading cons) var(0: THETA@v) var(1:THETA@v) latent(THETA) nocapslatent
+  	/* Stockage des estimations du modèle */
+  	estimates store modeldifB
+  	matrix val_mB = r(table)
+  	matrix esti_B = e(b)
+
+
+  	/* Calcul des difficultés d'item (delta_j) */
+  	matrix delta_mB=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+  		}
+  	}
+  	local name_partOneL ""
+
+  	forvalues j=1/`nbitems' {
+  			local name_partOneL "`name_partOneL' ``j''"
+  	}
+
+  	matrix colnames delta_mB = `name_partOneC'
+  	matrix rownames delta_mB = `name_partOneL'
+  		matrix delta_mB_se=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC_se ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+  		}
+  	}
+
+  	matrix colnames delta_mB_se = `name_partOneC_se'
+  	matrix rownames delta_mB_se = `name_partOneL'
+
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			forvalues g=0/1{
+  				qui lincom -[`p'.``j'']:`g'.`gp'
+  				local delta`j'_`p'g`g'mB=r(estimate)
+  				local delta`j'_`p'g`g'mB_se=r(se)
+  				if `p'>1{
+  					qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+  					local delta`j'_`p'g`g'mB = r(estimate)
+  					local delta`j'_`p'g`g'mB_se = r(se)
+  				}
+  				matrix delta_mB[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB'
+  				matrix delta_mB_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mB_se'
+  			}
+  		}
+  	}
+
+  	matrix var_mB = (val_mB[1,"/var(THETA)#0bn.`gp'"]\val_mB[2,"/var(THETA)#0bn.`gp'"])
+
+  	/*group effect*/
+  	qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
+  	local geffmB=r(estimate)
+  	local segeffmB=r(se)
+  	qui test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
+  	local gcmBp=r(p)
+  	local gcmBchi=r(chi2)
+  	local gcmBdf=r(df)
+
+
+  	*********************************
+  	**			MODEL A			   **
+  	*********************************
+
+  	local model ""
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			local model "`model' (`p'.``j''<-THETA@`p')"
+  		}
+  	}
+
+  	 qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading means) var(0: THETA@v) var(1:THETA@v) from(esti_B, skip) latent(THETA) nocapslatent
+
+  	/* Stockage des estimations du modèle */
+  	estimates store modeldifA
+  	matrix val_mA = r(table)
+  	matrix esti_A = e(b)
+
+  	/* Calcul des difficultés d'item (delta_j) */
+  	matrix delta_mA=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+  		}
+  	}
+  	local name_partOneL ""
+
+  	forvalues j=1/`nbitems' {
+  			local name_partOneL "`name_partOneL' ``j''"
+  	}
+  	matrix colnames delta_mA = `name_partOneC'
+  	matrix rownames delta_mA = `name_partOneL'
+  	matrix delta_mA_se=J(`nbitems',`=`nbdif_max'*2',.)
+  	local name_partOneC_se ""
+
+  	forvalues p=1/`nbdif_max' {
+  		forvalues g=0/1 {
+  			local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+  		}
+  	}
+
+  	matrix colnames delta_mA_se = `name_partOneC_se'
+  	matrix rownames delta_mA_se = `name_partOneL'
+
+  	forvalues j=1/`nbitems'{
+  		forvalues p=1/`nbdif_`j''{
+  			forvalues g=0/1{
+  				qui lincom -[`p'.``j'']:`g'.`gp'
+  				local delta`j'_`p'g`g'mA=r(estimate)
+  				local delta`j'_`p'g`g'mA_se=r(se)
+  				if `p'>1{
+  					qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+  					local delta`j'_`p'g`g'mA = r(estimate)
+  					local delta`j'_`p'g`g'mA_se = r(se)
+  				}
+  				matrix delta_mA[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA'
+  				matrix delta_mA_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mA_se'
+  			}
+  		}
+  	}
+  	//Variance et se mA
+  	matrix var_mA = (val_mA[1,"/var(THETA)#0bn.`gp'"]\val_mA[2,"/var(THETA)#0bn.`gp'"])
+
+	*********************************
+	*************MODEL C*************
+	*********************************
+	// Etape itérative si lrtest significatif
+	local nb_stepC = 0
+  qui lrtest modeldifA modeldifB
+  local diftestp=r(p)
+	if `diftestp'<0.05{  /*If pvalue(LRtest)<0.05 then step C*/
+		di
+		di as input "PROCESSING STEP C"
+		di
+
+		/*test DIF pour chaque item*/
+		local boucle = 1
+		local stop = 0
+		while `boucle'<=`=`nbitp'-1' & `stop'==0{ /*on s'arrête quand on a libéré du DIF sur (tous les items-1) ou lorsqu'il n'y a plus de tests significatifs*/
+			local nb_stepC = `boucle'
+			local pajust=0.05/`=`nbitp'+1-`boucle''
+			/*réinitialisation de la matrice de test*/
+			matrix test_difu_`boucle'=J(`nbitems',3,.)
+			matrix colnames test_difu_`boucle'=chi_DIFU df_DIFU pvalueDIFU
+			matrix test_dif_`boucle'=J(`nbitems',3,.)
+			matrix colnames test_dif_`boucle'=chi_DIF df_DIF pvalueDIF
+			local nbsig=0
+			local minpval=1
+			local itemdif=0
+			if "`detail'" != ""{
+
+				di as text "Loop `boucle'"
+				di as text _col(5) "Adjusted  alpha: " %6.4f  `pajust'
+				di
+				di as text _col(10) "{hline 65}"
+				di as text _col(10) "Freed item" _col(31) "Chi-Square"  _col(48) "DF" _col(57) "P-Value"
+				di as text _col(10) "{hline 65}"
+			}
+			/*boucle de test*/
+			forvalues j=1/`nbitems'{
+				//if `nbdif_`j'' > 2 {
+					local model ""
+					local listconst ""
+					if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF déjà détecté sur l'item j*/
+						/*on libère le DIF de l'item i: pas de contraintes*/
+						forvalues k=1/`nbitems'{ /*contraintes pour les autres items (si DIF NU sur item k, pas de contraintes*/
+							if `k'!=`j' & `nbmoda_`j'' >= 2 {
+								if dif_rc[`k',1]==. | dif_rc[`k',1]==0 {/*pas de DIF sur item k: contraintes 1-200*/
+									forvalues p=1/`nbdif_`k''{
+										qui local listconst "`listconst' `=0+`maxdif'*(`k'-1)+`p''"
+										qui constraint list `=0+`maxdif'*(`k'-1)+`p''
+									}
+								}
+								else{
+									if dif_rc[`k',2]!=. & dif_rc[`k',2]!= 0 & `nbmoda_`k'' > 2 { /*DIF U: contraintes 201-400*/
+										forvalues p=2/`nbdif_`k''{
+											qui local listconst "`listconst' `=200+`maxdif'*(`k'-1)+`p''"
+											qui constraint list `=200+`maxdif'*(`k'-1)+`p''
+										}
+									}
+								}
+							}
+						}
+						forvalues jj=1/`nbitems'{
+							forvalues p=1/`nbdif_`jj''{
+								local model "`model' (`p'.``jj''<-THETA@`p')"
+							}
+						}
+						qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
+						estimates store modeldif3b`boucle'it`i'
+
+						*************************
+						*****test DIF item i*****
+						*************************
+						qui test [1.``j'']1.`gp'=[1.``j'']0bn.`gp'
+						if `nbmoda_`j'' > 2 {
+							forvalues p=2/`nbdif_`j''{
+								qui test [`p'.``j'']1.`gp'=[`p'.``j'']0bn.`gp', acc
+							}
+						}
+						matrix test_dif_`boucle'[`j',1]=(r(chi2),r(df),r(p))
+
+						/* Test DIF Uniforme */
+						if `nbmoda_`j'' > 2 {
+							qui test 2*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[2.``j'']1.`gp'-[2.``j'']0bn.`gp'
+							forvalues p=3/`nbdif_`j''{
+								qui test `p'*([1.``j'']1.`gp'-[1.``j'']0bn.`gp')=[`p'.``j'']1.`gp'-[`p'.``j'']0bn.`gp', acc
+							}
+							matrix test_difu_`boucle'[`j',1]=(r(chi2), r(df), r(p))
+						}
+
+						if test_dif_`boucle'[`j',3]<`pajust'{/*si DIF sur item i*/
+							local ++nbsig
+							if test_dif_`boucle'[`j',3]<`minpval'{
+								local minpval=test_dif_`boucle'[`j',3]
+								local itemdif=`j'
+							}
+						}
+						if "`detail'" != "" {
+							di as text  _col(10) abbrev("``j''",15)  as result _col(31) %6.3f test_dif_`boucle'[`j',1] _col(48) test_dif_`boucle'[`j',2] _col(57)  %6.4f test_dif_`boucle'[`j',3]
+						}
+					}
+			}
+			/*si nb de tests significatifs=0, on arrête*/
+			if `nbsig'==0{
+				local stop=1
+				if `boucle' == 1 {
+					if "`detail'" != "" {
+						di as text _col(10) "{hline 65}"
+						di
+						di as result  "No significant test: no difference between groups detected, no DIF detected"
+						di
+					}
+				}
+				else {
+					if "`detail'" != ""{
+						di as text _col(10) "{hline 65}"
+						di
+						di as result "No other significant tests"
+						di
+					}
+				}
+			}
+			else{/*si nb de tests significatifs>0, mise à jour de la matrice de résultats*/
+				matrix dif_rc[`itemdif',1]=`boucle'
+				if "`detail'" != ""{
+					di as text _col(10) "{hline 65}"
+					di
+					di as result "Difference between groups on ``itemdif'' at time 1"
+				}
+				if `nbmoda_`itemdif'' > 2 {
+					if "`detail'" != "" {
+
+						di
+						di %~60s as text "Test of uniform difference"
+						di _col(10) "{hline 40}"
+						di _col(10) as text "Chi-square" _col(28) "DF" _col(40) "P-value"
+						di _col(10) as result %4.2f `=test_difu_`boucle'[`itemdif',1]' _col(28) `=test_difu_`boucle'[`itemdif',2]' _col(40) %4.2f `=test_difu_`boucle'[`itemdif',3]'
+						di _col(10) as text "{hline 40}"
+					}
+					if test_difu_`boucle'[`itemdif',3]<0.05{	/*DIF NU détectée*/
+						matrix dif_rc[`itemdif',2]=0
+							di
+							di as result "``itemdif'' : Non-uniform differences of item difficulties between groups at T1"
+							di
+					}
+					else{/*DIF U détectée*/
+						matrix dif_rc[`itemdif',2]=`boucle'
+							di
+							di as result "``itemdif'' : Uniform differences of item difficulties between groups at T1"
+							di
+					}
+				}
+				else {
+					// Différence entre groupes au temps 1 mais slmt 2 moda. donc pas de U ou NU
+					di _col(15) _dup(60) "-"
+				}
+			}
+			local ++boucle
+		}
+	}
+
+	/* MODELE FINAL DE LA PARTIE 1. Si DIFT1 détecté (=Au moins 2 boucles dans l'étape C)*/
+	if `nb_stepC' > 1 {
+		forvalues j=1/`nbitems'{
+			local model ""
+			local listconst ""
+			if dif_rc[`j',1]==. | dif_rc[`j',1]==0 { /*si pas de DIF: contraintes 1-200*/
+				forvalues p=1/`nbdif_`j''{
+					qui local listconst "`listconst' `=0+`maxdif'*(`j'-1)+`p''"
+					qui constraint list `=0+`maxdif'*(`j'-1)+`p''
+				}
+			}
+			else {
+				if dif_rc[`j',2]!=. & dif_rc[`j',2]!=0 { /*DIF U: contraintes 201-400*/
+					forvalues p=2/`nbdif_`j''{
+						qui local listconst "`listconst' `=200+`maxdif'*(`j'-1)+`p''"
+						qui constraint list `=200+`maxdif'*(`j'-1)+`p''
+					}
+				}
+			}
+		}
+		forvalues j=1/`nbitems'{
+			forvalues p=1/`nbdif_`j''{
+				local model "`model' (`p'.``j''<-THETA@`p')"
+			}
+		}
+
+		qui gsem `model', mlogit tol(0.01) iterate(100) group(`gp') ginvariant(coef loading) var(0: THETA@v) var(1:THETA@v) constraint(`listconst') from(esti_B) latent(THETA) nocapslatent
+		/* Stockage des estimations du modèle */
+		estimates store modeldifCFin
+		matrix val_mC = r(table)
+
+		/* Calcul des difficultés d'item (delta_j) */
+		matrix delta_mCFin=J(`nbitems',`=`nbdif_max'*2',.)
+		local name_partOneC ""
+		forvalues p=1/`nbdif_max' {
+			forvalues g=0/1 {
+				local name_partOneC "`name_partOneC' delta_`p'_gp`g'"
+			}
+		}
+		local name_partOneL ""
+		forvalues j=1/`nbitems' {
+			local name_partOneL "`name_partOneL' ``j''"
+		}
+		matrix colnames delta_mCFin = `name_partOneC'
+		matrix rownames delta_mCFin = `name_partOneL'
+
+		matrix delta_mCFin_se=J(`nbitems',`=`nbdif_max'*2',.)
+		local name_partOneC_se ""
+
+		forvalues p=1/`nbdif_max' {
+			forvalues g=0/1 {
+				local name_partOneC_se "`name_partOneC_se' delta_`p'_gp`g'_se"
+			}
+		}
+		matrix colnames delta_mCFin_se = `name_partOneC_se'
+		matrix rownames delta_mCFin_se = `name_partOneL'
+
+		forvalues j=1/`nbitems'{
+			forvalues p=1/`nbdif_`j''{
+				forvalues g=0/1{
+					qui lincom -[`p'.``j'']:`g'.`gp'
+					local delta`j'_`p'g`g'mCFin=r(estimate)
+					local delta`j'_`p'g`g'mCFin_se=r(se)
+					if `p'>1{
+						qui lincom [`=`p'-1'.``j'']:`g'.`gp' - [`p'.``j'']:`g'.`gp'
+						local delta`j'_`p'g`g'mCFin = r(estimate)
+						local delta`j'_`p'g`g'mCFin_se = r(se)
+					}
+					matrix delta_mCFin[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin'
+					matrix delta_mCFin_se[`j',`=2*`p'-1+`g'']=`delta`j'_`p'g`g'mCFin_se'
+				}
+			}
+		}
+		if "`group'" != "" { //Variance et se mA
+			matrix var_mC = (val_mC[1,"/var(THETA)#0bn.`gp'"]\val_mC[2,"/var(THETA)#0bn.`gp'"])
+		}
+			/*group effect*/
+		qui lincom [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'
+		local geffmCFin=r(estimate)
+		local segeffmCFin=r(se)
+		qui	test [/]:mean(THETA)#1.`gp'-[/]:mean(THETA)#0bn.`gp'=0
+		local gcmCFinp=r(p)
+		local gcmCFinchi=r(chi2)
+		local gcmCFindf=r(df)
+	}
+}
+
+	*********************************
+	 ***		BILAN  			***
+	*********************************
+
+	if "`group'" != "" & "`nodif'" == "" {
+	di
+	di %~84s as result "SUMMARY"
+	di as result _col(2)  "{hline 80}"
+	di as result 		 _col(18) "Difference in"
+	di as result _col(2) "Item" _col(18) "groups at T1" _col(36) "Recalibration"  _col(54) "RC " abbrev("`gp'",10) " 0"  _col(72) "RC " abbrev("`gp'",10) " 1"
+	di as result _col(2) "{hline 80}"
+	forvalues j=1/`nbitems' {
+		local RC
+		local RCg0
+		local RCg1
+		local difft1
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
+			local RC "Common"
+		}
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
+			local RC "Differential"
+		}
+		if `nbmoda_`j'' > 2 {
+			if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+				local RCg0 "Uniform"
+			}
+			if (dif_rc[`j',6] == 0) {
+				local RCg0 "Non-uniform"
+			}
+				if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
+				local RCg1 "Uniform"
+			}
+			if ( dif_rc[`j',8] == 0) {
+				local RCg1 "Non-uniform"
+			}
+			if (dif_rc[`j',1] != . ) {
+				if (dif_rc[`j',2]!=0) {
+					local difft1 "Uniform"
+				}
+				else {
+					local difft1 "Non-uniform"
+				}
+			}
+		}
+		else {
+			if dif_rc[`j',6] != . {
+				local RCg0 " X "
+			}
+			if dif_rc[`j',8] != . {
+				local RCg1 " X "
+			}
+			if dif_rc[`j',1] != . {
+				local difft1 " X "
+			}
+		}
+		di as result _col(2) abbrev("``j''",15) as text _col(18) "`difft1'"  _col(36) "`RC'"  _col(54) "`RCg0'"  _col(72) "`RCg1'"
+	}
+	di as result _col(2) "{hline 80}"
+	di
+}
+else if "`group'" != "" & "`nodif'" != "" {
+	di
+	di %~90s as result "SUMMARY"
+	di as result _col(10)  "{hline 70}"
+	di as result _col(10) "Item" _col(26) "Recalibration"  _col(46) "RC `gp' 0"  _col(62) "RC `gp' 1"
+	di _col(10) "{hline 70}"
+	forvalues j=1/`nbitems' {
+		local RC
+		local RCg0
+		local RCg1
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] == 0) {
+			local RC "Common"
+		}
+		if (dif_rc[`j',3] != . & dif_rc[`j',3] != 0 & dif_rc[`j',4] != 0) {
+			local RC "Differential"
+		}
+		if `nbmoda_`j'' > 2 {
+			if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+				local RCg0 "Uniform"
+			}
+			if (dif_rc[`j',6] == 0) {
+				local RCg0 "Non-uniform"
+			}
+			if (dif_rc[`j',8]!=. & dif_rc[`j',8] != 0) {
+				local RCg1 "Uniform"
+			}
+			if ( dif_rc[`j',8] == 0) {
+				local RCg1 "Non-uniform"
+			}
+		}
+		else {
+			if dif_rc[`j',6] != . {
+				local RCg0 " X "
+			}
+			if dif_rc[`j',8] != . {
+				local RCg1 " X "
+			}
+		}
+		di as result _col(10) "``j''" as text  _col(26) "`RC'"  _col(44) "`RCg0'"  _col(62) "`RCg1'"
+	}
+	di as result _col(10) "{hline 70}"
+}
+else if "`group'" == "" {
+	di
+	di %~60s as result "SUMMARY"
+	di as result _col(10)  "{hline 40}"
+	di _col(10) "Item" _col(36) "Recalibration"
+	di _col(10) "{hline 40}"
+	forvalues j=1/`nbitems' {
+		local RC
+		if dif_rc[`j',3] != . {
+			if `nbmoda_`j'' > 2 {
+				if (dif_rc[`j',6]!=. & dif_rc[`j',6] != 0) {
+					local RC "Uniform"
+				}
+				if (dif_rc[`j',6] == 0) {
+					local RC "Non-uniform"
+				}
+			}
+			else {
+				local RC " X "
+			}
+		}
+		di as result _col(10) "``j''" as text  _col(38) "`RC'"
+	}
+	di as result _col(10) "{hline 40}"
+	di
+}
+
+matrix dif_detect = J(1,`nbitems',.)
+local numdif=1
+forvalues j=1/`nbitems' {
+  if dif_rc[`j',1] != . {
+    matrix dif_detect[1,`numdif']=`j'
+    local numdif = `numdif'+1
+  }
+}
+return matrix difitems = dif_detect
+
+capture qui use `saverspcm', clear
+
+end
diff --git a/RProject/.Rhistory b/RProject/.Rhistory
index e88a674..af4c9a9 100644
--- a/RProject/.Rhistory
+++ b/RProject/.Rhistory
@@ -1,51 +1,3 @@
-beta.same.sign.truebeta.p=mean(beta.same.sign.truebeta.p),
-beta.same.sign.truebeta.signif.p=mean(beta.same.sign.truebeta.p[num.reject])
-)
-d <- cbind(b,a,z)
-d$prop.
-return(d)
-}
-#### Compiled results
-res.dat.dif <- compile_simulation2('5A_100')
-for (x in results[seq(2,length(results))]) {
-y <- compile_simulation2(x)
-res.dat.dif <- bind_rows(res.dat.dif,y)
-}
-#### Create data.frame
-results <- c(sapply(1:4,function(x) paste0(x,c('A','B','C','D','E'))),sapply(5:9,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
-results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
-results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
-results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
-results <- sort(results)
-results2 <- sort(results2)
-results <- c(results,results2)
-#### Compiler function
-compile_simulation <- function(scenario) {
-name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
-if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50" & name<=4) {
-s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N50/scenario_',scenario,'.csv'))
-}
-if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50" & name>4) {
-s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N50/scenario_',scenario,'.csv'))
-}
-if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name<=4) {
-s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'.csv'))
-}
-if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>4) {
-s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'_nodif.csv'))
-}
-if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name<=4) {
-s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'.csv'))
-}
-if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>4) {
-s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'_nodif.csv'))
-}
-if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name<=4) {
-s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'.csv'))
-}
-if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>4) {
-s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'_nodif.csv'))
-}
 if (unique(s$J)==4) {
 if (unique(s$M)==2) {
 a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4))
@@ -510,3 +462,51 @@ res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==50,]$theoretical.power
 res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==50,]$theoretical.power <- 0.4328
 ### DIF scenarios
 res.dat.dif$theoretical.power <- res.dat[81:nrow(res.dat),]$theoretical.power
+install.packages("lordif")
+library(lordif)
+library(TAM)
+library(doMC)
+library(parallel)
+library(pbmcapply)
+library(funprog)
+library(dplyr)
+library(readxl)
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N50/scenario_1A_50.csv')
+dat1 <- dat1[dat1$replication==1,]
+lordif(dat1,group=dat1$TT)
+dat1
+lordif(dat1,group=dat1$TT,selection = dat[,c('item1','item2',"item3",'item4')],model = "GPCM")
+lordif(dat1,group=dat1$TT,selection = dat1[,c('item1','item2',"item3",'item4')],model = "GPCM")
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM")
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N50/scenario_5A_50.csv')
+dat1 <- dat1[dat1$replication==1,]
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM")
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000)
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta")
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N300/scenario_5A_300.csv')
+dat1 <- dat1[dat1$replication==1,]
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta")
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N300/scenario_5D_300.csv')
+dat1 <- dat1[dat1$replication==1,]
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta")
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N300/scenario_7D_300.csv')
+dat1 <- dat1[dat1$replication==1,]
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta")
+dat1 <- dat1[dat1$replication<=20,]
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N300/scenario_7D_300.csv')
+dat1 <- dat1[dat1$replication==1,]
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta")
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GRM",maxIter = 10000,criterion = "Beta")
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta")
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta",MonteCarlo = T)
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta",inc=0.01)
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta",minCell=0.01)
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta",minCell=1)
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta",minCell=10)
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta")
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N300/scenario_7A_300.csv')
+dat1 <- dat1[dat1$replication<=20,]
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta")
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta",beta.change = 0.01)
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta",beta.change = 0.001)
+lordif(dat1[,c('item1','item2',"item3",'item4')],group=dat1$TT,model = "GPCM",maxIter = 10000,criterion = "Beta",beta.change = 0.2)