Organize DESCRIPTION and explicitly identify all functions from dependencies

10 files changed, 69 insertions, 69 deletions

diff --git a/DESCRIPTION b/DESCRIPTION
index 697cf71..0300ec0 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -9,12 +9,12 @@ Authors@R: c(
 Depends:
     R (>= 2.14.0),
 Imports:
-    rjags (>= 3-13),
     coda (>= 0.13),
+    graphics,
+    grDevices,
     parallel,
+    rjags (>= 3-13),
     stats,
-    grDevices,
-    graphics,
     utils
 Suggests: tinytest
 SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net)
diff --git a/NAMESPACE b/NAMESPACE
index c21a735..5ca5a20 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,22 +1,15 @@
-# import(lattice) # no longer needed?
-
-importFrom(rjags, "jags.model", "adapt", "coda.samples", "list.modules",
-           "load.module","unload.module","list.factories","set.factory")
-importFrom(coda, "gelman.diag", "as.mcmc.list", "thin", "mcmc", "as.mcmc")
-importFrom(parallel, "detectCores", "makeCluster", "clusterExport",
-           "clusterSetRNGStream", "clusterApply", "stopCluster", "clusterEvalQ")
-importFrom(stats, "runif", "time", "start", "end", "quantile", "var", "sd")
-importFrom(grDevices, "devAskNewPage", "rainbow")
-importFrom(graphics, "plot", "abline", "axis", "box", "segments", "lines", "par", "legend")
-importFrom(utils, "capture.output")
-
-export("jags", "jagsUI","jags.basic","autojags","pp.check", "traceplot",
-       "densityplot", "whiskerplot", "jags.View")
-
-S3method("plot", "jagsUI")
-# S3method("xyplot", "jagsUI")
-S3method("print","jagsUI")
-S3method("update","jagsUI")
-S3method("update","jagsUIbasic")
-S3method("summary","jagsUI")
-
+export(autojags)
+export(densityplot)
+export(jags)
+export(jagsUI)
+export(jags.basic)
+export(jags.View)
+export(pp.check)
+export(traceplot)
+export(whiskerplot)
+
+S3method(plot, "jagsUI")
+S3method(print,"jagsUI")
+S3method(summary,"jagsUI")
+S3method(update,"jagsUI")
+S3method(update,"jagsUIbasic")
diff --git a/R/autojags.R b/R/autojags.R
index 3b1f809..74da81d 100644
--- a/R/autojags.R
+++ b/R/autojags.R
@@ -86,7 +86,7 @@ autojags <- function(data,inits=NULL,parameters.to.save,model.file,n.chains,n.ad
     if(verbose){cat('Update ',index,' (',mcmc.info$n.iter + iter.increment,')',sep="")}
     
     if(save.all.iter){
-      if(index==2){start.iter <- start(samples)}
+      if(index==2){start.iter <- stats::start(samples)}
       if (index > 1) {
         old.samples <- samples
       }
@@ -192,7 +192,7 @@ test.Rhat <- function(samples,cutoff,params.omit,verbose=TRUE){
   expand <- sapply(strsplit(params, "\\["), "[", 1)
   
   gd <- function(hold){
-    r <- try(gelman.diag(hold, autoburnin=FALSE)$psrf[1], silent=TRUE)
+    r <- try(coda::gelman.diag(hold, autoburnin=FALSE)$psrf[1], silent=TRUE)
     if(inherits(r, "try-error") || !is.finite(r)) {
       r <- NA
     }
diff --git a/R/densityplot.R b/R/densityplot.R
index 2f7209b..1ca2f2f 100644
--- a/R/densityplot.R
+++ b/R/densityplot.R
@@ -26,7 +26,7 @@ param_density <- function(x, parameter, m_labels=FALSE){
   vals <- mcmc_to_mat(x$samples[,parameter])
  
   if(any(is.na(vals))){
-    plot(1:nrow(vals), rep(0, nrow(vals)), type='n',
+    graphics::plot(1:nrow(vals), rep(0, nrow(vals)), type='n',
          xlab='Value', ylab='Density', main=paste('Density of',parameter))
   } else {
     # Get bandwidth, one value for all chains
diff --git a/R/mcmc_tools.R b/R/mcmc_tools.R
index 1c867a1..4dabba8 100644
--- a/R/mcmc_tools.R
+++ b/R/mcmc_tools.R
@@ -94,11 +94,13 @@ bind.mcmc <- function(mcmc.list1,mcmc.list2,start,n.new.iter){
     
     d <- rbind(mcmc.list1[[i]],mcmc.list2[[i]])
     
-    samples[[i]] <- mcmc(data=d,start=start,end=(end(mcmc.list1[[i]])+n.new.iter),thin=thin(mcmc.list1[i]))
+    samples[[i]] <- coda::mcmc(data=d,start=start,
+                               end=(stats::end(mcmc.list1[[i]])+n.new.iter),
+                               thin=coda::thin(mcmc.list1[i]))
     
   }
   
-  return(as.mcmc.list(samples))
+  return(coda::as.mcmc.list(samples))
   
   
 }
diff --git a/R/process_input.R b/R/process_input.R
index 15991b2..8092750 100644
--- a/R/process_input.R
+++ b/R/process_input.R
@@ -150,7 +150,7 @@ check_inits <- function(inits, n_chains){
   # If not add them
   if(!has_RNG){
     # Generate random seeds for each chain
-    chain_seeds <- floor(runif(n_chains, 1, 100000))
+    chain_seeds <- floor(stats::runif(n_chains, 1, 100000))
     for (i in 1:n_chains){
       inits[[i]]$.RNG.name="base::Mersenne-Twister"
       inits[[i]]$.RNG.seed=chain_seeds[i]
diff --git a/R/process_output.R b/R/process_output.R
index a17ff48..68b66fb 100644
--- a/R/process_output.R
+++ b/R/process_output.R
@@ -169,14 +169,14 @@ calc_neff <- function(mcmc_list){
   mcmc_mat <- mcmc_to_mat(mcmc_list)
   
   xdot <- apply(mcmc_mat, 2, mean, na.rm=TRUE)
-  s2 <- apply(mcmc_mat, 2, var, na.rm=TRUE)
+  s2 <- apply(mcmc_mat, 2, stats::var, na.rm=TRUE)
   W <- mean(s2)
 
   #Non-degenerate case
   if(is.na(W)){
 	  n_eff <- NA
   } else if ((W > 1.e-8) && (nchain > 1)) {
-    B <- niter * var(xdot)
+    B <- niter * stats::var(xdot)
     sig2hat <- ((niter-1)*W + B)/ niter
     n_eff <- round(nchain * niter * min(sig2hat/B,1),0)
   } else {
diff --git a/R/run_rjags.R b/R/run_rjags.R
index 2cbe51a..0b107f4 100644
--- a/R/run_rjags.R
+++ b/R/run_rjags.R
@@ -53,16 +53,18 @@ run.model <- function(model.file=NULL, data=NULL, inits=NULL, parameters.to.save
     if(verbose | parallel==TRUE){
       m$recompile()
     } else {
-      null <- capture.output(m$recompile())
+      null <- utils::capture.output(m$recompile())
     }
 
   } else {
     #Compile model
     if(verbose | parallel==TRUE){
-      m <- jags.model(file=model.file,data=data,inits=inits,n.chains=n.chains,n.adapt=0)
+      m <- rjags::jags.model(file=model.file,data=data,
+                             inits=inits,n.chains=n.chains,n.adapt=0)
     } else {
-      null <- capture.output(
-      m <- jags.model(file=model.file,data=data,inits=inits,n.chains=n.chains,n.adapt=0,quiet=TRUE)
+      null <- utils::capture.output(
+      m <- rjags::jags.model(file=model.file,data=data,inits=inits,
+                             n.chains=n.chains,n.adapt=0,quiet=TRUE)
       )
     }
   }
@@ -75,11 +77,14 @@ run.model <- function(model.file=NULL, data=NULL, inits=NULL, parameters.to.save
       if(verbose){
         cat('Adaptive phase,',n.adapt,'iterations x',n.chains,'chains','\n')
         cat('If no progress bar appears JAGS has decided not to adapt','\n','\n')
-        sufficient.adapt <- adapt(object=m, n.iter=n.adapt, progress.bar=pb, end.adaptation=TRUE)
+        sufficient.adapt <- rjags::adapt(object=m, n.iter=n.adapt, 
+                                         progress.bar=pb, end.adaptation=TRUE)
       } else {
-        null <- capture.output(
-        sufficient.adapt <- adapt(object=m, n.iter=n.adapt, progress.bar=pb, end.adaptation=TRUE)
-        )}
+        null <- utils::capture.output(
+        sufficient.adapt <- rjags::adapt(object=m, n.iter=n.adapt, 
+                                         progress.bar=pb, end.adaptation=TRUE)
+        )
+      }
       total.adapt <- total.adapt + n.adapt
     } else{
       if(verbose){cat('No adaptive period specified','\n','\n')}
@@ -87,10 +92,10 @@ run.model <- function(model.file=NULL, data=NULL, inits=NULL, parameters.to.save
       #Force JAGS to not adapt (you have to allow it to adapt at least 1 iteration)
       if(!update){
         if(verbose){
-          sufficient.adapt <- adapt(object=m, n.iter=1, end.adaptation=TRUE)
+          sufficient.adapt <- rjags::adapt(object=m, n.iter=1, end.adaptation=TRUE)
         } else {
-          null <- capture.output(
-            sufficient.adapt <- adapt(object=m, n.iter=1, end.adaptation=TRUE)
+          null <- utils::capture.output(
+            sufficient.adapt <- rjags::adapt(object=m, n.iter=1, end.adaptation=TRUE)
           )}
       }
       total.adapt <- 0
@@ -102,17 +107,17 @@ run.model <- function(model.file=NULL, data=NULL, inits=NULL, parameters.to.save
 
     for (i in 1:maxloops){
       if(verbose) cat('Adaptive phase.....','\n')
-      sufficient.adapt <- adapt(object=m, n.iter=n.adapt.iter, progress.bar='none')
+      sufficient.adapt <- rjags::adapt(object=m, n.iter=n.adapt.iter, progress.bar='none')
       total.adapt <- total.adapt + n.adapt.iter
       if(i==maxloops){
         if(verbose){
           warning(paste("Reached max of",maxloops*n.adapt.iter,"adaption iterations; set n.adapt to > 10000"))
         }
-        null <- adapt(object=m,n.iter=1,end.adaptation = TRUE)
+        null <- rjags::adapt(object=m,n.iter=1,end.adaptation = TRUE)
         break
       }
       if(sufficient.adapt){
-        null <- adapt(object=m,n.iter=1,end.adaptation = TRUE)
+        null <- rjags::adapt(object=m,n.iter=1,end.adaptation = TRUE)
         if(verbose) cat('Adaptive phase complete','\n','\n')
         break
       }
@@ -130,7 +135,7 @@ run.model <- function(model.file=NULL, data=NULL, inits=NULL, parameters.to.save
       update(object=m,n.iter=n.burnin,progress.bar=pb)
       cat('\n')
     } else {
-      null <- capture.output(
+      null <- utils::capture.output(
       update(object=m,n.iter=n.burnin,progress.bar=pb)
     )}
   } else if(verbose){
@@ -141,15 +146,15 @@ run.model <- function(model.file=NULL, data=NULL, inits=NULL, parameters.to.save
   if(verbose){
     cat('Sampling from joint posterior,',(n.iter-n.burnin),
         'iterations x',n.chains,'chains','\n','\n')
-    samples <- coda.samples(model=m, variable.names=parameters.to.save,
-                            n.iter=(n.iter-n.burnin), thin=n.thin,
-                            na.rm=na.rm, progress.bar=pb)
+    samples <- rjags::coda.samples(model=m, variable.names=parameters.to.save,
+                                   n.iter=(n.iter-n.burnin), thin=n.thin,
+                                   na.rm=na.rm, progress.bar=pb)
     cat('\n')
   } else {
-    null <- capture.output(
-    samples <- coda.samples(model=m, variable.names=parameters.to.save,
-                            n.iter=(n.iter-n.burnin), thin=n.thin,
-                            na.rm=na.rm, progress.bar=pb)
+    null <- utils::capture.output(
+    samples <- rjags::coda.samples(model=m, variable.names=parameters.to.save,
+                                   n.iter=(n.iter-n.burnin), thin=n.thin,
+                                   na.rm=na.rm, progress.bar=pb)
     )
   }
 
@@ -169,10 +174,10 @@ run.parallel <- function(data=NULL, inits=NULL, parameters.to.save, model.file=N
   current.libpaths <- .libPaths()
 
   #Set up clusters
-  cl <- makeCluster(n.cores)
-  on.exit(stopCluster(cl))
-  clusterExport(cl = cl, ls(), envir = environment())
-  clusterEvalQ(cl,.libPaths(current.libpaths))
+  cl <- parallel::makeCluster(n.cores)
+  on.exit(parallel::stopCluster(cl))
+  parallel::clusterExport(cl = cl, ls(), envir = environment())
+  parallel::clusterEvalQ(cl,.libPaths(current.libpaths))
 
   if(verbose){
     cat('Beginning parallel processing using',n.cores,
@@ -210,7 +215,7 @@ run.parallel <- function(data=NULL, inits=NULL, parameters.to.save, model.file=N
     }
 
   #Do parallel analysis
-  par <- clusterApply(cl=cl, x=1:n.chains, fun=jags.clust)
+  par <- parallel::clusterApply(cl=cl, x=1:n.chains, fun=jags.clust)
 
   #Create empty lists
   out <- samples <- model <- list()
@@ -229,7 +234,7 @@ run.parallel <- function(data=NULL, inits=NULL, parameters.to.save, model.file=N
     model[[i]] <- par[[i]]$m
     sufficient.adapt[i] <- par[[i]]$sufficient.adapt
   }
-  out$samples <- as.mcmc.list(samples)
+  out$samples <- coda::as.mcmc.list(samples)
   
   # Remove columns with all NA
   try({
@@ -260,10 +265,10 @@ set.factories <- function(factories){
       split <- strsplit(factories[i],'\\s')[[1]]
     
       #Check if requested factory is available
-      faclist <- as.character(list.factories(split[2])[,1])
+      faclist <- as.character(rjags::list.factories(split[2])[,1])
       if(split[1]%in%faclist){
         
-        null <- set.factory(split[1],split[2],split[3])
+        null <- rjags::set.factory(split[1],split[2],split[3])
       
       } else{stop(paste('Requested factory',split[1],'is not available. Check that appropriate modules are loaded.'))}
     
@@ -277,22 +282,22 @@ set.modules <- function(modules,DIC){
 
   #Load/unload appropriate modules (besides dic)
   called.set <- c('basemod','bugs',modules)
-  current.set <- list.modules()
+  current.set <- rjags::list.modules()
 
   load.set <- called.set[!called.set%in%current.set]
   unload.set <- current.set[!current.set%in%called.set]
 
   if(length(load.set)>0){
     for (i in 1:length(load.set)){
-      load.module(load.set[i],quiet=TRUE)
+      rjags::load.module(load.set[i],quiet=TRUE)
     }
   }
   if(length(unload.set)>0){
     for (i in 1:length(unload.set)){
-      unload.module(unload.set[i],quiet=TRUE)
+      rjags::unload.module(unload.set[i],quiet=TRUE)
     }
   }
   if(DIC){
-    load.module("dic",quiet=TRUE)
+    rjags::load.module("dic",quiet=TRUE)
   }
 }
diff --git a/R/traceplot.R b/R/traceplot.R
index 66069fb..6ee9b4b 100644
--- a/R/traceplot.R
+++ b/R/traceplot.R
@@ -32,7 +32,7 @@ param_trace <- function(x, parameter, m_labels=FALSE){
   cols <- grDevices::rainbow(ncol(vals))
 
   if(all(is.na(vals))){
-    plot(1:nrow(vals), rep(0, nrow(vals)), type='n',
+    graphics::plot(1:nrow(vals), rep(0, nrow(vals)), type='n',
               xlab="Iterations", ylab="Value",
               main=bquote(.(parameter)*","~hat(R) == .(Rhat)))
   } else {
diff --git a/R/update.R b/R/update.R
index 048a0c3..02690c0 100644
--- a/R/update.R
+++ b/R/update.R
@@ -99,7 +99,7 @@ update.jagsUIbasic <- function(object, parameters.to.save=NULL,
   # Set up MCMC info
   mcmc.info <- list(n.chains = length(object$samples), n.adapt = n.adapt, 
                     n.iter = n.iter, n.burnin = 0,
-                    n.thin = ifelse(is.null(n.thin), thin(object$samples), n.thin),
+                    n.thin = ifelse(is.null(n.thin), coda::thin(object$samples), n.thin),
                     n.cores = object$n.cores)
 
   parallel <- names(object$model[1]) == "cluster1"