diff --git a/R/pcm.R b/R/pcm.R
index d4d2b61..f96427b 100644
--- a/R/pcm.R
+++ b/R/pcm.R
@@ -10,6 +10,7 @@
 #' @param grp string containing the name of the column where an optional group membership variable is stored in df
 #' @param dif.items vector containing the list of indexes in "items" corresponding to dif items
 #' @param type.dif vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF
+#' @param weights string containing the name of the column where optional weights are stored in df
 #' @param verbose set to TRUE to print a detailed output, FALSE otherwise
 #' @param fit string determining the optimization algorithm. Values "ucminf" or "nlminb" ar recommended
 #' @param method.theta string determining the estimation method for individual latent variable values. Either "eap", "mle" or "wle"
@@ -18,7 +19,7 @@
 #' @import PP
 #' @export
 
-pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,verbose=T,fit="ucminf",method.theta="eap") {
+pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,weights=NULL,verbose=T,fit="ucminf",method.theta="eap") {
   ##### Detecting errors
 
   if (any(!(items %in% colnames(df)))) {
@@ -69,14 +70,22 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,verbose
       print(df)
       colnames(df)[2:(length(colnames(df)))] <- paste0("item",seq(1,length(colnames(df))-1))
       df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
-      colnames(df.long) <- c("id","item","resp")
+      if (is.null(weights)) {
+        colnames(df.long) <- c("id","item","resp")
+      } else {
+        colnames(df.long) <- c("id","item","resp","weights")
+      }
       nbitems <- length(2:(length(colnames(df))))
       maxmod <- max(df[,2:(length(colnames(df)))])
       df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-1),ordered = F)
       df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
       df.long$id <- factor(df.long$id)
       # fit pcm
-      mod <- olmm(resp ~ 0 + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"))
+      if (is.null(weights)) {
+        mod <- olmm(resp ~ 0 + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"))
+      } else {
+        mod <- olmm(resp ~ 0 + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),weights = df.long$weights)
+      }
       comod <- coef(mod)
       # output results
       restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-1,nbitems)]))
@@ -102,7 +111,11 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,verbose
       df <- df[,c('id',items,"grp")]
       colnames(df)[2:(length(colnames(df))-1)] <- paste0("item",seq(1,length(colnames(df))-2))
       df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
-      colnames(df.long) <- c("id","grp","item","resp")
+      if (is.null(weights)) {
+        colnames(df.long) <- c("id","grp","item","resp")
+      } else {
+        colnames(df.long) <- c("id","grp","item","resp","weights")
+      }
       nbitems <- length(2:(length(colnames(df))-1))
       maxmod <- max(df[,2:(length(colnames(df))-1)])
       df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F)
@@ -119,7 +132,11 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,verbose
       # fit pcm
       formudif <- paste0("resp ~ 0 + ge(grp",ifelse(length(difvar.unif>0),"+",""),ifelse(length(difvar.unif>0),paste0(difvar.unif,":grp",collapse="+"),""),")+ce(item",ifelse(length(difvar.nonunif>0),"+",""),ifelse(length(difvar.nonunif)>0,paste0(difvar.nonunif,":grp",collapse="+"),""),")+re(0|id)")
       formudif <- as.formula(formudif)
-      mod <- olmm(formudif,data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit))
+      if (is.null(weights)) {
+        mod <- olmm(formudif,data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit))
+      } else {
+        mod <- olmm(formudif,data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit),weights = df.long$weights)
+      }
       comod <- coef(mod)
       # output results
       nbcoef <- nbitems+length(difvar.nonunif)
@@ -183,14 +200,22 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,verbose
         df <- df[,c('id',items,"grp")]
         colnames(df)[2:(length(colnames(df))-1)] <- paste0("item",seq(1,length(colnames(df))-2))
         df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
-        colnames(df.long) <- c("id","grp","item","resp")
+        if (is.null(weights)) {
+          colnames(df.long) <- c("id","grp","item","resp")
+        } else {
+          colnames(df.long) <- c("id","grp","item","resp","weights")
+        }
         nbitems <- length(2:(length(colnames(df))-1))
         maxmod <- max(df[,2:(length(colnames(df))-1)])
         df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F)
         df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
         df.long$id <- factor(df.long$id)
         # fit pcm
-        mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit))
+        if (is.null(weights)) {
+          mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit))
+        } else {
+          mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit),weights=df.long$weights)
+        }
         comod <- coef(mod)
         # output results
         restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-2,nbitems)]))
diff --git a/man/pcm.Rd b/man/pcm.Rd
index ec90d33..46b892b 100644
--- a/man/pcm.Rd
+++ b/man/pcm.Rd
@@ -10,6 +10,7 @@ pcm(
   grp = NULL,
   dif.items = NULL,
   type.dif = NULL,
+  weights = NULL,
   verbose = T,
   fit = "ucminf",
   method.theta = "eap"
@@ -26,6 +27,8 @@ pcm(
 
 \item{type.dif}{vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF}
 
+\item{weights}{string containing the name of the column where optional weights are stored in df}
+
 \item{verbose}{set to TRUE to print a detailed output, FALSE otherwise}
 
 \item{fit}{string determining the optimization algorithm. Values "ucminf" or "nlminb" ar recommended}