update handling of self=FALSE in getParents and use in setupMargNodes

UserManual/src/_output.yml

@@ -5,7 +5,7 @@ bookdown::gitbook:
       before: |
         <img src="./nimble-icon.png"
              width=100>
-        <li><a href="./cha-welcome-nimble.html">NIMBLE User Manual, Version 1.4.2</a></li>
+        <li><a href="./cha-welcome-nimble.html">NIMBLE User Manual, Version 1.4.3</a></li>
         <li><a href="https://github.com/nimble-dev/nimble">NIMBLE Development Team</a></li>
         <li><a href="https://R-nimble.org">https://R-nimble.org</a></li>
       after: |

UserManual/src/chapter_MCMC.Rmd

@@ -758,17 +758,16 @@ derived_name <- nimbleFunction(
       ## 
       ## run fuction has one argument:
       ## timesRan, which is the number of times this method has executed during
-      ##   the present MCMC chain,
-      ## including the present time.  This can be used as an index for a matrix
-      ##   of results.
+      ##   the present MCMC chain, including the present time.  This can be used
+      ##   as an index for a matrix of results.
       ## 
     },
     methods = list(
         set_interval = function(newInterval = double()) {
           ##
           ## REQUIRED `set_interval` method, which matches the definition below
           ##   exactly.
-          ## this method is necessary, to allow the default value of 'interval' to
+          ## This method is necessary, to allow the default value of 'interval' to
           ## match the thinning interval of the MCMC.
           ## 
           interval <<- newInterval
@@ -777,7 +776,7 @@ derived_name <- nimbleFunction(
         before_chain = function(niter = double(), nburnin = double(),
                          thin = double(1), chain = double()) {
           ## 
-          ## (optional) `before_chain` method,
+          ## (optional) `before_chain` method:
           ## must have must have these arguments specifications:
           ## - niter (total number of *post-burnin* MCMC iterations)
           ## - nburnin (number of burnin iterations for this MCMC chain)
@@ -788,7 +787,7 @@ derived_name <- nimbleFunction(
         },
         after_chain = function() {
           ## 
-          ## (optional) `after_chain` method,
+          ## (optional) `after_chain` method:
           ## no arguments and no return value
           ## 
         },
@@ -808,7 +807,7 @@ derived_name <- nimbleFunction(
         },
         reset = function() {
           ## 
-          ## (optional) `reset` method
+          ## (optional) `reset` method:
           ## no arguments and no return value
           ## 
         }

UserManual/src/title.tex

@@ -4,7 +4,7 @@
 \vspace{0.2in}
 {\large NIMBLE Development Team}\\
 \vspace{0.2in}
-{\large Version 1.4.2}\\
+{\large Version 1.4.3}\\
 \vspace{3in}
 \includegraphics[width=1.5in]{../nimble-icon.png}\\
 {\large

packages/nimble/DESCRIPTION

@@ -16,7 +16,7 @@ Description: A system for writing hierarchical statistical models largely
     one can use 'NIMBLE' for writing arbitrary other kinds of model-generic
     algorithms as well. A full User Manual is available at <https://r-nimble.org>.
 Version: 1.4.3
-Date: 2026-05-01
+Date: 2026-08-21
 Maintainer: Christopher Paciorek <paciorek@stat.berkeley.edu>
 Authors@R: c(
     person("Perry", "de Valpine", role = "aut"),
@@ -153,4 +153,4 @@ Collate:
     registration.R
     nimble-package.r
     zzz.R
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3

packages/nimble/INSTALL

@@ -1,9 +1,9 @@
 You can install NIMBLE as an R package from CRAN in the usual way.
 
 You can install from the source file using
-   R CMD INSTALL nimble_1.4.2.tar.gz
+   R CMD INSTALL nimble_1.4.3.tar.gz
 or
-  install.packages("nimble_1.4.2.tar.gz", repos = NULL)
+  install.packages("nimble_1.4.3.tar.gz", repos = NULL)
 
 You can also install the package directly from the NIMBLE repository 
   install.packages("nimble", repos = "https://r-nimble.org")
@@ -21,7 +21,7 @@ i.e. not Windows.
     By default, we will use the Eigen distribution included with the package.
 
 We can specify any or all of these with
-  R CMD INSTALL nimble_1.4.2.tar.gz  --configure-args="--enable-dylib=true --with-eigen=/Users/duncan/local"
+  R CMD INSTALL nimble_1.4.3.tar.gz  --configure-args="--enable-dylib=true --with-eigen=/Users/duncan/local"
 or
   install.packages("nimble", configure.args = "--enable-dylib=true --with-eigen=/Users/duncan/local")
 

packages/nimble/R/BUGS_model.R

@@ -959,7 +959,9 @@ Details: The upward search for dependent nodes propagates through deterministic
   if(self)	{ # The C++ call does *not* return self nodes
     nodeFunIDs <- unique(modelDef$maps$vertexID_2_nodeID[ nodeIDs ])
     parentIDs <- sort(c(parentIDs, nodeFunIDs))
-  }
+  } else parentIDs <- setdiff(parentIDs, nodeIDs)
+
+
   if(!includeRHSonly) parentIDs <- parentIDs[modelDef$maps$types[parentIDs] != 'RHSonly']
   if(determOnly)      parentIDs <- parentIDs[modelDef$maps$types[parentIDs] == 'determ']
   if(stochOnly)	      parentIDs <- parentIDs[modelDef$maps$types[parentIDs] == 'stoch']

packages/nimble/R/cppDefs_ADtools.R

@@ -2,6 +2,8 @@
 #'
 #' create an ADproxyModelClass object. For internal use.
 #'
+#' @usage ADproxyModelClass(Rmodel)
+#' 
 #' @param Rmodel The name of an uncompiled model
 #' 
 #' @author NIMBLE development team

packages/nimble/R/nimbleFunction_Rexecution.R

@@ -107,6 +107,8 @@ nimSeq <- function(from, to, by, length.out) { ## this creates default arguments
 #' 
 #' @name setupOutputs
 #'
+#' @usage setupOutputs(...)
+#'
 #' @param ... An arbitrary set of names
 #'
 #' @details

packages/nimble/R/nimbleList_core.R

@@ -27,6 +27,8 @@ nimbleListBase <- setRefClass(Class = 'nimbleListBase',
 #'
 #' Create a nimbleType object, with information on the name, type, and dimension of an object to be placed in a \code{\link{nimbleList}}.
 #'
+#' @usage nimbleType(name, type, dim = NA)
+#'
 #' @param name The name of the object, given as a character string.
 #' @param type The type of the object, given as a character string.
 #' @param dim  The dimension of the object, given as an integer.  This can be left blank if the object is a nimbleList.

packages/nimble/R/setupMargNodes.R

@@ -91,15 +91,19 @@
 #'   \code{calcNodes}. Such deterministic nodes will be included in
 #'   calculations automatically when needed.
 #'
-#' If \code{randomEffectsNodes} is missing, the default is a bit complicated: it
-#'   includes all latent nodes that are descendants (or "downstream") of
-#'   \code{paramNodes} (if provided) and are either (i) ancestors (or
-#'   "upstream") of data nodes (if \code{calcNodes} is missing), or (ii)
-#'   ancestors or elements of \code{calcNodes} (if \code{calcNodes} and
-#'   \code{paramNodes} are provided), or (iii) elements of \code{calcNodes} (if
-#'   \code{calcNodes} is provided but \code{paramNodes} is missing). In all
-#'   cases, discrete nodes (with warning if \code{check=TRUE}), posterior
-#'   predictive nodes and \code{paramNodes} are excluded.
+#'  If \code{randomEffectsNodes} is missing, the default is a bit complicated.
+#'    It starts by considering all latent nodes as potential \code{randomEffectsNodes}.
+#'    If \code{paramNodes} is provided, only latent nodes that are downstream of
+#'    \code{paramNodes} are included in the potential nodes.
+#'    Then the set of potential nodes is filtered to
+#'    include only nodes that satisfy one of the following conditions
+#'    (only one of which will be applicable): (i) parents of data nodes
+#'    (applies only if calcNodes is missing), (ii) parents or elements of
+#'    \code{calcNodes} (applies only if \code{calcNodes} and \code{paramNodes}
+#'    are both provided), (iii) elements of calcNodes (applies if \code{calcNodes}
+#'    is provided but \code{paramNodes} is missing). In all
+#'    cases, discrete nodes (with warning if \code{check=TRUE}), posterior
+#'    predictive nodes and \code{paramNodes} are excluded.
 #'
 #' \code{randomEffectsNodes} should only include stochastic nodes.
 #'
@@ -234,8 +238,6 @@ setupMargNodes <- function(model, paramNodes, randomEffectsNodes, calcNodes,
     } else {
       nodesToFindParentsFrom <- randomEffectsNodes
       paramNodes <- model$getParents(nodesToFindParentsFrom, self=FALSE, stochOnly=TRUE)
-      # self=FALSE doesn't omit if one RE node is a parent of another, so we have to do the next step
-      paramNodes <- setdiff(paramNodes, nodesToFindParentsFrom)
     }
     if(paramsHandled) {
       if(calcProvided) paramNodes <- setdiff(paramNodes, calcNodes)
@@ -283,21 +285,18 @@ setupMargNodes <- function(model, paramNodes, randomEffectsNodes, calcNodes,
         tempDataNodesDefault <- model$getNodeNames(dataOnly = TRUE)
       if(paramsHandled)
         tempDataNodesDefault <- setdiff(tempDataNodesDefault, paramNodes)
-      tempDataNodesDefaultParents <- model$getParents(tempDataNodesDefault, upstream = TRUE, stochOnly = TRUE)
-      # See comment above about why this is necessary:
-      tempDataNodesDefaultParents <- setdiff(tempDataNodesDefaultParents, tempDataNodesDefault)
+      tempDataNodesDefaultParents <- model$getParents(tempDataNodesDefault, upstream = TRUE, stochOnly = TRUE,
+                                                      self = FALSE)
       reNodesDefault <- intersect(reNodesDefault, tempDataNodesDefaultParents)
     } else {
       # Update reNodesDefault to exclude nodes that lack downstream connection to a calcNode
       if(paramsHandled) { # This means reProvided OR paramsProvided. Including parents allows checking
         # of potentially missing REs.
         reNodesDefault <- intersect(reNodesDefault,
-                                    model$getParents(calcNodes, upstream=TRUE, stochOnly = TRUE))
+                                    model$getParents(calcNodes, upstream=TRUE, stochOnly = TRUE, self = TRUE))
       } else { # This means !paramsHandled and hence !reProvided AND !paramsProvided
         reNodesDefault <- intersect(reNodesDefault,
                                     calcNodes)
-        reNodesDefault <- intersect(reNodesDefault,
-                                    model$getParents(calcNodes, upstream=TRUE, stochOnly = TRUE))
       }
     }
   }
@@ -306,8 +305,6 @@ setupMargNodes <- function(model, paramNodes, randomEffectsNodes, calcNodes,
   # and are now ready to create default paramNodes
   if(!paramsHandled) {
     paramNodes <- model$getParents(reNodesDefault, self=FALSE, stochOnly=TRUE)
-    # See comment above about why this is necessary:
-    paramNodes <- setdiff(paramNodes, reNodesDefault)
     if(calcOtherProvided) paramNodes <- setdiff(paramNodes, calcNodesOther)
   }
 
@@ -414,8 +411,6 @@ setupMargNodes <- function(model, paramNodes, randomEffectsNodes, calcNodes,
   if(!paramProvided) {
     possibleNewParamNodes <- model$getParents(calcNodes, self=FALSE, stochOnly=TRUE, includeData=FALSE)
     # includeData=FALSE as data nodes cannot be parameters
-    # self=FALSE doesn't omit if one node is a parent of another, so we have to do the next step
-    possibleNewParamNodes <- setdiff(possibleNewParamNodes, calcNodesDefault)
     paramNodes <- unique(c(paramNodes, possibleNewParamNodes))
   }
 

packages/nimble/tests/testthat/test-getDependencies.R

@@ -154,12 +154,12 @@ test_that("getParents works in model with no criss-crossing dependencies", {
 
   expect_identical(m1$getParents("f1"), c("a1", "a2", "c2", "c4", "c3"))
   expect_identical(m1$getParents("f1", immediateOnly = TRUE), c("c4", "c3"))
-  expect_identical(m1$getParents(c("f1", "g1"), stochOnly = TRUE), c("a1", "a2", "f1"))
-  expect_identical(m1$getParents(c("f1", "g1"), immediateOnly = TRUE), c("c4", "c3", "f1"))
-  expect_identical(m1$getParents(c("g1", "f1"), stochOnly = TRUE), c("a1", "a2", "f1"))
+  expect_identical(m1$getParents(c("f1", "g1"), stochOnly = TRUE), c("a1", "a2"))
+  expect_identical(m1$getParents(c("f1", "g1"), immediateOnly = TRUE), c("c4", "c3"))
+  expect_identical(m1$getParents(c("g1", "f1"), stochOnly = TRUE), c("a1", "a2"))
   expect_identical(m1$getParents(c("f1", "g1"), stochOnly = TRUE, self = TRUE), c("a1", "a2", "f1", "g1"))
-  expect_identical(m1$getParents(c("c3", "c2", "e1"), stochOnly = FALSE), c("a1", "c2", "c3"))
-  expect_identical(m1$getParents(c("c3", "c2", "e1"), immediateOnly = TRUE, stochOnly = FALSE), c("a1", "c2", "c3"))
+  expect_identical(m1$getParents(c("c3", "c2", "e1"), stochOnly = FALSE), c("a1"))
+  expect_identical(m1$getParents(c("c3", "c2", "e1"), immediateOnly = TRUE, stochOnly = FALSE), c("a1"))
   expect_identical(m1$getParents("h1", includeRHSonly = TRUE, stochOnly = FALSE), c("lho"))
 })