if(identical(Sys.getenv("NOT_CRAN"), "true")& .Machine$sizeof.pointer != 4){
  
  library(ctsem)
  library(testthat)
  
  context("ukfpopcheck") 
  
  test_that("ukfpopcheck1", {
    set.seed(2)
    Tpoints<-10
    n.latent=2
    n.manifest=3
    nsubjects=60
    burnin=3
    dtmat = matrix(exp(rnorm(burnin+Tpoints-1,-.3,.5)),1)
    par1<-rnorm(nsubjects,-.3,.8)
    par2 <- par1*.4 + rnorm(nsubjects,0,.3)
    for(i in 1:nsubjects){
      gm<-suppressMessages(ctModel(type='omx',n.latent=2,n.manifest=n.manifest,Tpoints=Tpoints,LAMBDA=matrix(c(1,.4,0,0,0,1),3,ncol=2),
        DRIFT=diag(-.4,2),
        CINT=matrix(c(par1[i],par2[i]),2),
        T0VAR=diag(1,2),
        T0MEANS=matrix(c(30,50),n.latent),
        MANIFESTVAR=t(chol(diag(.5,n.manifest))),
        DIFFUSION=t(chol(diag(3,2)))))
      if(i==1) cd<-suppressMessages(ctGenerate(gm,n.subjects=1,burnin=burnin,wide=FALSE,dtmat = dtmat)) else {
        newdat <- suppressMessages(ctGenerate(gm,n.subjects=1,burnin=burnin,wide=FALSE,dtmat = dtmat))
        newdat[,'id'] <- i
        cd<-rbind(cd,newdat)
      }
    }
    

    m1<-ctModel(type='omx',n.latent=4,n.manifest=n.manifest,Tpoints=Tpoints,
      LAMBDA=cbind(gm$LAMBDA,0,0),
      MANIFESTMEANS=matrix(0,nrow=n.manifest)
    )
    m1$DRIFT[3:4,] <- 0
    m1$DRIFT[,3:4] <- 0
    diag(m1$DRIFT)[3:4] <- -1e-5
    m1$DRIFT[1,3] = 1
    m1$DRIFT[2,4] = 1
    m1$DIFFUSION[3:4,] <- 0
    m1$DIFFUSION[,3:4] <- 0
    # m1$T0VAR[3:4,1:2] <- 0
    
    #model for ukf ctsem
    m2<-ctModel(type='omx',n.latent=2,n.manifest=n.manifest,Tpoints=Tpoints,
      MANIFESTMEANS=matrix(0,n.manifest),
      CINT=matrix(paste0('cint',1:gm$n.latent)),
      TRAITVAR='auto',
      LAMBDA=gm$LAMBDA
    )
    
    
    
    #bayesian / ukf ctsem
    sm1 <- ctStanModel(m1)
    sm1$pars$indvarying <- FALSE

    sf1 <- ctStanFit(cd,sm1,iter=200,
      optimize=TRUE,verbose=0,
      optimcontrol=list(finishsamples=50),
      nlcontrol=list(nldynamics=FALSE))

    
    sf1d=sf1$stanfit$transformedparsfull$pop_DRIFT[1,1:2,1:2]#matrix(sf1$stanfit$transformedpars_old[grep('pop_DRIFT',rownames(sf1$stanfit$transformedpars_old)),'mean'],4,4)[1:2,1:2]
    sf1ll=sf1$stanfit$optimfit$value
    
    sm2 <- ctStanModel(m2)
    sm2$pars$sdscale <- .2
    
    sf2 <- ctStanFit(cd,sm2,
      optimize=T,verbose=0)

    sf2d=sf2$stanfit$transformedparsfull$pop_DRIFT[1,1:2,1:2]# matrix(sf2$stanfit$transformedpars_old[grep('pop_DRIFT',rownames(sf2$stanfit$transformedpars_old)),'mean'],4,4)[1:2,1:2]
    sf2ll=sf2$stanfit$optimfit$value
    
    if(1==99){
    summary(sf2)$popmeans
    sf2$stanfit$transformedparsfull$popcov[1,,]
    sf1$stanfit$transformedparsfull$pop_T0cov[1,,]
    }
    
    for(i in 1:4){
      for(j in 1:4){
        if(i==j) testthat::expect_equivalent(
             sqrt(sf1$stanfit$transformedparsfull$pop_T0cov[1,i,j])/ sqrt(sf2$stanfit$transformedparsfull$popcov[1,i,j]),
          1,
          tol=.15)
        if(i>j) testthat::expect_equivalent(  cov2cor(    sf1$stanfit$transformedparsfull$pop_T0cov[1,,])[i,j],
          cov2cor(sf2$stanfit$transformedparsfull$popcov[1,,])[i,j],.1)
      }
    }
    
    dvec=c('sf1d','sf2d')
    llvec=c('sf1ll','sf2ll')
    
    sapply(dvec,get,envir=sys.frame(sys.parent(0)))
    sapply(llvec,get,envir=sys.frame(sys.parent(0)))
    
    for(di in 2:length(dvec)){
      expect_equivalent(get(dvec[di]),get(dvec[di-1]),tol=1e-1)
      expect_equivalent(get(llvec[di]),get(llvec[di-1]),tol=1e-3)
      expect_equivalent(get(dvec[di]),c(-.4,0,0,-.4),tol=.1)
    }
    
  })
  
  
  
  test_that("ukfpopcheck2_randomdrift", {
    set.seed(1)
    nsubjects=60
    Tpoints=20
    driftsd=.2
    driftmu= log(.5)
    dt=1
    drift= -2*log1p(exp(2*(rnorm(nsubjects,driftmu,driftsd))))
    
    for(si in 1:nsubjects){
      m=suppressMessages(ctModel(LAMBDA=diag(1), Tpoints=Tpoints, DRIFT=matrix(drift[si]),T0MEANS = matrix(3), 
        T0VAR=matrix(sqrt(.1)), DIFFUSION=diag(1,1), CINT=matrix(-2),MANIFESTVAR=matrix(sqrt(.1))))
      d=suppressMessages(ctGenerate(m,n.subjects = 1,burnin = 0,wide = FALSE,dtmean = dt))[,-1]
      if(si==1) dat=cbind(si,d) else dat=rbind(dat,cbind(si,d))
    }
    
    colnames(dat)[1]='id'
    
    dtm <- ctModel(LAMBDA=diag(1), type='stanct',
      DRIFT=matrix('dr11| -2*log1p(exp(-2*param))'),
      CINT=matrix('cint'),
      MANIFESTMEANS = matrix(0)
    )
    
    dtm$pars$indvarying <- FALSE
    dtm$pars$indvarying[dtm$pars$matrix %in% 'DRIFT'] <- TRUE
    
    dtf = ctStanFit(datalong = dat,ctstanmodel = dtm,optimize=TRUE,
      verbose=0,optimcontrol=list(estonly=F),savescores = F)
    s1=summary(dtf,parmatrices = F,priorcheck = F,residualcov = F)
    
    dtm2 <- ctModel(LAMBDA=matrix(c(1,0),1,2), type='stanct',
      DIFFUSION=matrix(c('diff',0,0,0),2,2),DRIFT=matrix(c('-2*log1p(exp(-2*state[2]))',0,0,-.00001),2,2),
      CINT=matrix(c('cint',0),2,1),T0MEANS=matrix(c('t0m1','t0m2|param'),2,1),
      T0VAR=matrix(c('t0v11',0,0,paste0('t0var22 | ',gsub(".* sdscale","",gsub('rawpopsdbase','param',dtm$rawpopsdtransform),fixed=TRUE))),2,2),
      MANIFESTMEANS = matrix(0))
    
    dtm2$pars$indvarying <- FALSE
    dtf2=ctStanFit(datalong = dat,ctstanmodel = dtm2,optimize = TRUE)
    s2=summary(dtf2,parmatrices = F,priorcheck = F,residualcov = F)
    
    # expect_equivalent(c(s1$popmeans[,1],s1$popsd[,1]),s2$popmeans[,1],tol=1e-2)
    testthat::expect_equivalent(sort(dtf2$stanfit$rawest),sort(dtf$stanfit$rawest),tol=1e-3) #sorting is an ugly hack! could improve...
    
  })
  
}