* using log directory ‘/srv/hornik/tmp/CRAN_pretest/scalablebayesm.Rcheck’
* using R Under development (unstable) (2025-02-18 r87748)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    Debian clang version 19.1.7 (1+b1)
    Debian flang-new version 19.1.7 (1+b1)
* running under: Debian GNU/Linux trixie/sid
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* checking for file ‘scalablebayesm/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘scalablebayesm’ version ‘0.2’
* package encoding: UTF-8
* checking CRAN incoming feasibility ... [3s/3s] NOTE
Maintainer: ‘Federico Bumbaca <federico.bumbaca@colorado.edu>’

New submission

Possibly misspelled words in DESCRIPTION:
  Bumbaca (19:104)
  Misra (19:124)
  Rossi (19:137)
  Scalable (19:188)
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* checking examples ... [8s/8s] ERROR
Running examples in ‘scalablebayesm-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: rhierMnlDPParallel
> ### Title: MCMC Algorithm for Hierarchical Multinomial Logit with Dirichlet
> ###   Process Prior Heterogeneity
> ### Aliases: rhierMnlDPParallel
> 
> ### ** Examples
> 
> 
> 
> set.seed(66)
> R = 500
> 
> p = 3                                # num of choice alterns
> ncoef = 3  
> nlgt = 1000                           # num of cross sectional units
> nz = 2
> Z = matrix(runif(nz*nlgt), ncol=nz)
> Z = t(t(Z)-apply(Z,2,mean))          # demean Z
> ncomp = 3                            # no of mixture components
> Delta=matrix(c(1,0,1,0,1,2), ncol=2)
> 
> comps = NULL
> comps[[1]] = list(mu=c(0,-1,-2),   rooti=diag(rep(2,3)))
> comps[[2]] = list(mu=c(0,-1,-2)*2, rooti=diag(rep(2,3)))
> comps[[3]] = list(mu=c(0,-1,-2)*4, rooti=diag(rep(2,3)))
> pvec=c(0.4, 0.2, 0.4)
> 
> ##  simulate from MNL model conditional on X matrix
> simmnlwX = function(n,X,beta) {
+   k = length(beta)
+   Xbeta = X%*%beta
+   j = nrow(Xbeta) / n
+   Xbeta = matrix(Xbeta, byrow=TRUE, ncol=j)
+   Prob = exp(Xbeta)
+   iota = c(rep(1,j))
+   denom = Prob%*%iota
+   Prob = Prob / as.vector(denom)
+   y = vector("double", n)
+   ind = 1:j
+   for (i in 1:n) {
+     yvec = rmultinom(1, 1, Prob[i,])
+     y[i] = ind%*%yvec}
+   return(list(y=y, X=X, beta=beta, prob=Prob))
+ }
> 
> ## simulate data with a mixture of 3 normals
> simlgtdata = NULL
> ni = rep(50,nlgt)
> for (i in 1:nlgt) {
+   betai = Delta%*%Z[i,] + as.vector(bayesm::rmixture(1,pvec,comps)$x)
+   Xa = matrix(runif(ni[i]*p,min=-1.5,max=0), ncol=p)
+   X = bayesm::createX(p, na=1, nd=NULL, Xa=Xa, Xd=NULL, base=1)
+   outa = simmnlwX(ni[i], X, betai)
+   simlgtdata[[i]] = list(y=outa$y, X=X, beta=betai)
+ }
> 
> ## plot betas
> 
> if(0){
+   oldpar = par()
+   bmat = matrix(0, nlgt, ncoef)
+   for(i in 1:nlgt) { bmat[i,] = simlgtdata[[i]]$beta }
+   par(mfrow = c(ncoef,1))
+   for(i in 1:ncoef) { hist(bmat[,i], breaks=30, col="magenta") }
+   par(oldpar)
+ }
> 
> ## set Data and Mcmc lists
> keep = 5
> Mcmc1 = list(R=R, keep=keep)
> Prior1=list(ncomp=1)
> Data1 = list(p=p, lgtdata=simlgtdata, Z=Z)
> Data2 = partition_data(Data = list(Data1), s = 1)
> 
> out = parallel::mclapply(Data2, FUN = rhierMnlDPParallel,
+ Prior = Prior1, Mcmc = Mcmc1, mc.cores = 1, mc.set.seed = FALSE)
Error in FUN(X[[i]], ...) : object 'nvar' not found
Calls: <Anonymous> -> lapply -> FUN -> matrix
Execution halted
* checking PDF version of manual ... [3s/3s] OK
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* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE
Status: 1 ERROR, 1 NOTE