Flavor: r-devel-linux-x86_64-debian-gcc
Check: examples, Result: ERROR
  Running examples in 'abn-Ex.R' failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: buildScoreCache
  > ### Title: Build a cache of goodness of fit metrics for each node in a DAG,
  > ###   possibly subject to user-defined restrictions
  > ### Aliases: buildScoreCache buildScoreCache.bayes forLoopContentBayes
  > ###   forLoopContent buildScoreCache.mle
  > ### Keywords: buildScoreCache.bayes buildScoreCache.mle calc.node.inla.glm
  > ###   calc.node.inla.glmm fitAbn.bayes fitAbn.mle internal models
  >
  > ### ** Examples
  >
  > ## Simple example
  > # Generate data
  > N <- 1e6
  > mydists <- list(a="gaussian",
  +                 b="gaussian",
  +                 c="gaussian")
  > a <- rnorm(n = N, mean = 0, sd = 1)
  > b <- 1 + 2*rnorm(n = N, mean = 5, sd = 1)
  > c <- 2 + 1*a + 2*b + rnorm(n = N, mean = 2, sd = 1)
  > mydf <- data.frame("a" = scale(a),
  +                    "b" = scale(b),
  +                    "c" = scale(c))
  >
  > # ABN with MLE
  > mycache.mle <- buildScoreCache(data.df = mydf,
  +                                data.dists = mydists,
  +                                method = "mle",
  +                             max.parents = 2)
  Loading required package: Matrix
  > dag.mle <- mostProbable(score.cache = mycache.mle,
  +                         max.parents = 2)
  Step1. completed max alpha_i(S) for all i and S
  Total sets g(S) to be evaluated over: 8
  > myfit.mle <- fitAbn(object = dag.mle,
  +                     method = "mle",
  +                     max.parents = 2)
  > plot(myfit.mle)
  >
  > # ABN with Bayes
  > mycache.bayes <- buildScoreCache(data.df = mydf,
  +                                  data.dists = mydists,
  +                                  method = "bayes",
  +                                  max.parents = 2)
  Error in library(p, character.only = TRUE) :
    there is no package called 'INLA'
  Calls: buildScoreCache ... buildScoreCache.bayes -> %do% -> <Anonymous> -> library
  Execution halted

test_that("plot.abnDag() works.", {
  mydag <- createAbnDag(dag = ~a+b|a, data.df = data.frame("a"=1, "b"=1))

  if(.Platform$OS.type == "unix") {
    FILE <- "/dev/null"
  } else {
    FILE <- "nul"
  }
  capture.output({
    expect_no_error({
      plot(mydag)
      })
    },
    file = FILE)
})

> mycache.mle$mlik
[1] -1418.438      -Inf
> logLik(modglm)
'log Lik.' -1410.645 (df=2)

load(system.file("extdata", "QA_glm_case1_data.RData", package = "abn")) # or download from here: http://r-bayesian-networks.org/source/Rcode/QA_glm_case2.tar.gz

## 1. plot of raw differences, a wide range of values since both poisson, bin and gaus distributions used.
## vast majority as almost identical, but some are rather different
#plot(mycache.inla$mlik-mycache.c$mlik);

## 2. also look at % differences - gives a crude overview
## as 1. so suggests perhaps not just floating point rounding issue e.g. in log transforms
perc<-100*(mycache.c$mlik-mycache.inla$mlik)/mycache.c$mlik;

## 3. get all mliks which are adrift by more than 1%
bad<-which(abs(perc)>1);

## go through each and check for issues
## 
mydat<-ex2.dag.data;## this data comes with abn see ?ex2.dag.data
mydat.std<-mydat;
## setup distribution list for each node
mydists<-list(b1="binomial",
              g1="gaussian",
              p1="poisson",
              b2="binomial",
              g2="gaussian",
              p2="poisson",
              b3="binomial",
              g3="gaussian",
              p3="poisson",
              b4="binomial",
              g4="gaussian",
              p4="poisson",
              b5="binomial",
              g5="gaussian",
              p5="poisson",
              b6="binomial",
              g6="gaussian",
              p6="poisson"
             );
## create standardised dataset for comparison with glm
for(i in 1:length(mydists)){if(mydists[[i]]=="gaussian"){## then std data for comparison with glm_case
                                                            mydat.std[,i]<-(mydat.std[,i]-mean(mydat.std[,i]))/sd(mydat.std[,i]);}
}
## create empty matrix which will be filled with nodes as needed
mydag<-matrix(rep(0,dim(mydat)[2]^2),ncol=dim(mydat)[2]);colnames(mydag)<-rownames(mydag)<-names(mydat);

## loop through each node which differed from INLA by at least 1% and compare with glm() modes
for(i in 1:length(bad)){

  mydag[,]<-0;## reset
  node<-mycache.c$child[bad[i]];pars<-mycache.c$node.defn[bad[i],];
  form<-as.formula(paste(colnames(mydag)[node],"~",paste(colnames(mydag)[which(pars==1)],collapse="+",sep=""),sep=""));
  family<-mydists[[node]];
  mydag[node,]<-pars;## copy "bad" node into DAG
  myres.c<-fitabn(dag.m=mydag,data.df=mydat,data.dists=mydists,max.mode.error=0,compute.fixed=TRUE);## use C
  myres.inla<-fitabn(dag.m=mydag,data.df=mydat,data.dists=mydists,max.mode.error=100,compute.fixed=TRUE,n.grid=NULL,std.area=FALSE);## use INLA
  myres.glm<-glm(form,data=mydat.std,family=family);
  cat("################ bad=",i,"#################\n");
  cat("\n# 1. glm()\n");print(coef(myres.glm));
  cat("\n# 2. C\n");print(myres.c$modes[[node]]);
  cat("\n# 3. INLA\n");print(myres.inla$modes[[node]]);
  cat("\n###########################################\n");
}

Flavor: r-devel-linux-x86_64-debian-gcc
Check: re-building of vignette outputs, Result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building 'data_simulation.Rmd' using rmarkdown
  
  Quitting from lines 29-58 [fit_model] (data_simulation.Rmd)
  Error: processing vignette 'data_simulation.Rmd' failed with diagnostics:
  there is no package called 'INLA'
  --- failed re-building 'data_simulation.Rmd'
  
  --- re-building 'mixed_effect_BN_model.Rmd' using rmarkdown
  --- finished re-building 'mixed_effect_BN_model.Rmd'
  
  --- re-building 'model_specification.Rmd' using rmarkdown
  --- finished re-building 'model_specification.Rmd'
  
  --- re-building 'multiprocessing.Rmd' using rmarkdown
  
  Quitting from lines 88-130 [benchmarking] (multiprocessing.Rmd)
  Error: processing vignette 'multiprocessing.Rmd' failed with diagnostics:
  worker initialization failed: there is no package called 'INLA'
  --- failed re-building 'multiprocessing.Rmd'
  
  --- re-building 'paper.Rmd' using rmarkdown
  --- finished re-building 'paper.Rmd'
  
  --- re-building 'parameter_learning.Rmd' using rmarkdown
  
  Quitting from lines 67-72 [unnamed-chunk-3] (parameter_learning.Rmd)
  Error: processing vignette 'parameter_learning.Rmd' failed with diagnostics:
  there is no package called 'INLA'
  --- failed re-building 'parameter_learning.Rmd'
  
  --- re-building 'quick_start_example.Rmd' using rmarkdown
  --- finished re-building 'quick_start_example.Rmd'
  
  --- re-building 'structure_learning.Rmd' using rmarkdown
  --- finished re-building 'structure_learning.Rmd'
  
  SUMMARY: processing the following files failed:
    'data_simulation.Rmd' 'multiprocessing.Rmd' 'parameter_learning.Rmd'
  
  Error: Vignette re-building failed.
  Execution halted

### Generate data
# Set seed for reproducibility
set.seed(123)

# Number of groups
n_groups <- 5

# Number of observations per group
n_obs_per_group <- 100

# Total number of observations
n_obs <- n_groups * n_obs_per_group

# Simulate group effects
group <- factor(rep(1:n_groups, each = n_obs_per_group))
group_effects <- rnorm(n_groups)

# Simulate variables
G1 <- rnorm(n_obs) + group_effects[group]
B1 <- rbinom(n_obs, 1, plogis(group_effects[group]))
G2 <- 1.5 * B1 + 0.7 * G1 + rnorm(n_obs) + group_effects[group]
B2 <- rbinom(n_obs, 1, plogis(2 * G2 + group_effects[group]))

# Create data frame
data <- data.frame(group = group, G1 = G1, G2 = G2, B1 = factor(B1), B2 = factor(B2))

# Look at data
str(data)
summary(data)

######
# Reproduce issue
######
### method = "mle"
# OK: Build the score cache with 2 parents for each variable
score_cache <- buildScoreCache(data.df = data,
                               data.dists = list(G1 = "gaussian", 
                                                 G2 = "gaussian", 
                                                 B1 = "binomial", 
                                                 B2 = "binomial"),
                               group.var = "group",
                               max.parents = 2,
                               method = "mle")

# BUG: Build the score cache with different number of parents for each variable
score_cache <- buildScoreCache(data.df = data,
                               data.dists = list(G1 = "gaussian", 
                                                 G2 = "gaussian", 
                                                 B1 = "binomial", 
                                                 B2 = "binomial"),
                               group.var = "group",
                               max.parents = list(G1 = 0, G2 = 2, B1 = 0, B2 = 3),
                               method = "mle")

### method = "bayes"
# OK: Build the score cache with different number of parents for each variable
score_cache <- buildScoreCache(data.df = data,
                               data.dists = list(G1 = "gaussian", 
                                                 G2 = "gaussian", 
                                                 B1 = "binomial", 
                                                 B2 = "binomial"),
                               group.var = "group",
                               max.parents = list(G1 = 0, G2 = 2, B1 = 0, B2 = 3),
                               method = "bayes")

  # get data
  mydat <- ex5.dag.data[,-19] ## get the data - drop group variable

  # Restrict DAG
  banned<-matrix(c(
    # 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
    0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b1
    1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b2
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b3
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b4
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b5
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b6
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g1
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g2
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g3
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g4
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g5
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g6
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g7
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g8
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g9
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g10
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g11
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 # g12
  ),byrow=TRUE,ncol=18)

  colnames(banned)<-rownames(banned)<-names(mydat)

  retain<-matrix(c(
    # 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b1
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b2
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b3
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b4
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b5
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # b6
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g1
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g2
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g3
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g4
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g5
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g6
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g7
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g8
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g9
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g10
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, # g11
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 # g12
  ),byrow=TRUE,ncol=18)
  ## again must set names
  colnames(retain)<-rownames(retain)<-names(mydat)

  # set distributions
  mydists<-list(b1="binomial",
                b2="binomial",
                b3="binomial",
                b4="binomial",
                b5="binomial",
                b6="binomial",
                g1="gaussian",
                g2="gaussian",
                g3="gaussian",
                g4="gaussian",
                g5="gaussian",
                g6="gaussian",
                g7="gaussian",
                g8="gaussian",
                g9="gaussian",
                g10="gaussian",
                g11="gaussian",
                g12="gaussian"
  )

  # Compute score cache
  mycache.1par <- buildScoreCache(data.df=mydat,data.dists=mydists, max.parents=1,centre=TRUE)

  # Estimate most probable DAG
  mp.dag <- mostProbable(score.cache = mycache.1par)

Found the following (possibly) invalid URLs:
    URL: http://aje.oxfordjournals.org/content/176/11/1051.abstract (moved to https://academic.oup.com/aje/article-abstract/176/11/1051/178588)
      From: README.md
      Status: 301
      Message: Moved Permanently
    URL: http://aje.oxfordjournals.org/content/176/11/1051.full.pdf?keytype=ref&ijkey=zCJD2Zt88XaDYyY (moved to https://academic.oup.com/aje/article-pdf/176/11/1051/428801/kws183.pdf?keytype=ref&ijkey=zCJD2Zt88XaDYyY)
      From: README.md
      Status: 301
      Message: Moved Permanently
    URL: http://download.springer.com/static/pdf/949/art%253A10.1186%252Fs12917-016-0649-0.pdf?originUrl=http%3A%2F%2Fbmcvetres.biomedcentral.com%2Farticle%2F10.1186%2Fs12917-016-0649-0&token2=exp=1455044551~acl=%2Fstatic%2Fpdf%2F949%2Fart%25253A10.1186%25252Fs12917-016-0649-0.pdf*~hmac=e04039a7400eefea35dc05635bccae1688e549b8b0eb36edc0b8fd72caba73fc
      From: README.md
      Status: 404
      Message: Not Found
    URL: http://mcmc-jags.sourceforge.net/ (moved to https://mcmc-jags.sourceforge.io/)
      From: README.md
      Status: 301
      Message: Moved Permanently
    URL: http://pdn.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271186&_user=4429&_pii=S0167587711000341&_check=y&_origin=browseVolIssue&_zone=rslt_list_item&_coverDate=2011-06-15&wchp=dGLbVlS-zSkWb&md5=29522e1462a0ac05fe07c787a4cd3d0a&pid=1-s2.0-S0167587711000341-main.pdf
      From: README.md
      Status: Error
      Message: Could not resolve host: pdn.sciencedirect.com
    URL: http://web.cs.iastate.edu/~jtian/cs673/cs673_spring05/references/Friedman-Koller-2003.pdf (moved to https://faculty.sites.iastate.edu/jtian/)
      From: README.md
      Status: 301
      Message: Moved Permanently
    URL: http://www.bioconductor.org/ (moved to https://www.bioconductor.org/)
      From: README.md
      Status: 301
      Message: Moved Permanently
    URL: http://www.bioconductor.org/packages/release/bioc/html/Rgraphviz.html (moved to https://www.bioconductor.org/packages/release/bioc/html/Rgraphviz.html)
      From: README.md
      Status: 301
      Message: Moved Permanently
    URL: http://www.ete-online.com/content/10/1/4 (moved to https://link.springer.com/journal/12982)
      From: README.md
      Status: 301
      Message: Moved Permanently
    URL: http://www.r-inla.org/ (moved to https://www.r-inla.org/)
      From: README.md
      Status: 301
      Message: Moved Permanently
    URL: https://r-bayesian-networks.org/quick_start_example.html
      From: inst/doc/paper.html
      Status: Error
      Message: schannel: SNI or certificate check failed: SEC_E_WRONG_PRINCIPAL (0x80090322) - Der Zielprinzipalname ist falsch.
    For content that is 'Moved Permanently', please change http to https,
    add trailing slashes, or replace the old by the new URL.
  
  Found the following (possibly) invalid file URI:
    URI: quick_start_example.md
      From: README.md

diff --git a/R/build_score_cache_mle.R b/R/build_score_cache_mle.R
index 6e3e650..5b03b3f 100755
--- a/R/build_score_cache_mle.R
+++ b/R/build_score_cache_mle.R
@@ -377,6 +377,9 @@ buildScoreCache.mle <-
 
     ############################## Function to create the cache
 
+    if ( length(max.parents) == 1 ) {
+        max.parents <- rep(max.parents, nvars)
+    }
 
     if (!is.null(defn.res)) {
         max.parents <- max(apply(defn.res[["node.defn"]], 1, sum))
@@ -392,83 +395,64 @@ buildScoreCache.mle <-
             return(v)
         }
 
-        node.defn <- matrix(data = as.integer(0), nrow = 1L, ncol = nvars)
-        children <- 1
+        ## Generate all possible bit patterns for n variables, with a maximum of m 1s
+        generateBitPatterns = function(n, m) {
+          z <- rep(0,n)
+          do.call(rbind, lapply(0:m, function(i) t(apply(combn(1:n,i), 2, function(k) {z[k]=1;z}))))
+        }
 
-        for (j in 1:nvars) {
-            if (j != 1) {
-                node.defn <- rbind(node.defn, matrix(data = as.integer(0),
-                                                     nrow = 1L, ncol = nvars))
-                children <- cbind(children, j)
-            }
-            # node.defn <- rbind(node.defn,matrix(data = 0,nrow = 1,ncol = n))
+        # Function to generate all possible combinations of parents
+        filteredCombinations = function(x, m, bannedParents, retainedParents) {
+          # These are the parents that cannot change
+          fixedParents = bannedParents | retainedParents | (fun.return(x, length(x) + 2) + 1) %% 2
+          # These are the parents that can change
+          parentPossibleChoices = which(fixedParents == 0)
+          numPossibleChoices = length(parentPossibleChoices)
+          numRetainedParents = sum(retainedParents)
+
+          # Generate all possible combinations of parents, taking account of banned, retained and maximum number of parents
+          parentChoices = generateBitPatterns(numPossibleChoices, min(m-numRetainedParents, numPossibleChoices)) == 1
+          output = t(apply(parentChoices, 1, function(pc) {
+            combinedRow = 1L*(retainedParents | fun.return(parentPossibleChoices[pc], length(x) + 2))
+            combinedRow
+          }))
+          output
+        }
+
+        children <- matrix(nrow=1, ncol=0)
+        node.defn.list = list()
 
+        for (j in 1:nvars) {
           if(is.list(max.parents)){
             stop("ISSUE: `max.parents` as list is not yet implemented further down here. Try with a single numeric value as max.parents instead.")
             if(!is.null(which.nodes)){
               stop("ISSUE: `max.parents` as list in combination with `which.nodes` is not yet implemented further down here. Try with single numeric as max.parents instead.")
             }
-          } else if (is.numeric(max.parents) && length(max.parents)>1){
-            if (length(unique(max.parents)) == 1){
-              max.parents <- unique(max.parents)
-            } else {
-              stop("ISSUE: `max.parents` with node specific values that are not all the same, is not yet implemented further down here.")
-            }
-          }
-
-          if(max.parents == nvars){
-            max.parents <- max.parents-1
-            warning(paste("`max.par` == no. of variables. I set it to (no. of variables - 1)=", max.parents)) #NOTE: This might cause differences to method="bayes"!
           }
 
-            for (i in 1:(max.parents)) {
-                tmp <- t(combn(x = (nvars - 1), m = i, FUN = fun.return, n = nvars, simplify = TRUE))
-                tmp <- t(apply(X = tmp, MARGIN = 1, FUN = function(x) append(x = x, values = 0, after = j - 1)))
-
-                node.defn <- rbind(node.defn, tmp)
-
-                # children position
-                children <- cbind(children, t(rep(j, length(tmp[, 1]))))
-            }
+            # The parents that are banned and retained for node j
+            bannedParents = dag.banned[j, ]
+            retainedParents = dag.retained[j, ]
+            # All possible parents for node j, which is all nodes except j
+            parentChoice = c(seq.int(from=1, length.out=j-1), seq.int(from=j+1, length.out=nvars-j))
+            # How many parents we are keeping for node j
+            numRetainedParents = sum(retainedParents)
+            # The maximum number of parents for node j
+            m = max.parents[j]
+
+            # Generate all possible combinations of parents for node j
+            tmp <- filteredCombinations(x = parentChoice, m=m, bannedParents=bannedParents, retainedParents=retainedParents)
+            # We need a sparse matrix here to deal with large numbers of variables, otherwise memory usage if very high.
+            tmp2 = Matrix(tmp, sparse = TRUE)
+            node.defn.list[[length(node.defn.list) + 1]] <- tmp2
+            children <- cbind(children, t(rep(j, length(tmp2[, 1]))))
         }
 
-        # children <- rowSums(node.defn)
+        node.defn = do.call(rbind, node.defn.list)
         colnames(node.defn) <- colnames(data.df)
-        ## Coerce numeric matrix into integer matrix !!!
-        node.defn <- apply(node.defn, c(1, 2), function(x) {
-            (as.integer(x))
-        })
-
         children <- as.integer(children)
         # node.defn_ <- node.defn
 
-        ## DAG RETAIN/BANNED
-        for (i in 1:nvars) {
-            for (j in 1:nvars) {
-
-                ## DAG RETAIN
-                if (dag.retained[i, j] != 0) {
-                  tmp.indices <- which(children == i & node.defn[, j] == 0)
-
-                  if (length(tmp.indices) != 0) {
-                    node.defn <- node.defn[-tmp.indices, ]
-                    children <- children[-tmp.indices]
-                  }
-                }
-
-                ## DAG BANNED
-                if (dag.banned[i, j] != 0) {
-                  tmp.indices <- which(children == i & node.defn[, j] == 1)
-
-                  if (length(tmp.indices) != 0) {
-                    node.defn <- node.defn[-tmp.indices, ]
-                    children <- children[-tmp.indices]
-                  }
-                }
-
-            }
-        }
-
         mycache <- list(children = as.integer(children), node.defn = (node.defn))
 
         ###------------------------------###

Flavor: r-devel-linux-x86_64-debian-gcc
Check: package dependencies, Result: NOTE
  Package suggested but not available for checking: 'INLA'

Flavor: r-devel-linux-x86_64-debian-gcc
Check: examples, Result: ERROR
  Running examples in 'abn-Ex.R' failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: print.abnCache
  > ### Title: Print objects of class 'abnCache'
  > ### Aliases: print.abnCache
  >
  > ### ** Examples
  >
  > ## Subset of the build-in dataset, see  ?ex0.dag.data
  > mydat <- ex0.dag.data[,c("b1","b2","g1","g2","b3","g3")] ## take a subset of cols
  >
  > ## setup distribution list for each node
  > mydists <- list(b1="binomial", b2="binomial", g1="gaussian",
  +                 g2="gaussian", b3="binomial", g3="gaussian")
  >
  > # Structural constraints
  > # ban arc from b2 to b1
  > # always retain arc from g2 to g1
  >
  > ## parent limits
  > max.par <- list("b1"=2, "b2"=2, "g1"=2, "g2"=2, "b3"=2, "g3"=2)
  >
  > ## now build the cache of pre-computed scores accordingly to the structural constraints
  >
  > res.c <- buildScoreCache(data.df=mydat, data.dists=mydists,
  +                          dag.banned= ~b1|b2, dag.retained= ~g1|g2, max.parents=max.par)
  Error in library(p, character.only = TRUE) :
    there is no package called 'INLA'
  Calls: buildScoreCache ... buildScoreCache.bayes -> %do% -> <Anonymous> -> library
  Execution halted