Merge pull request #145 from StochasticTree/cran-valgrind-hotfix

Fixed R examples with uninitialized random effects terms (which triggered valgrind errors in CRAN checks)

Author: andrewherren

DESCRIPTION

@@ -49,5 +49,6 @@ SystemRequirements: C++17
 Imports: 
     R6,
     stats
-URL: https://stochtree.ai
+URL: https://stochtree.ai/, https://github.com/StochasticTree/stochtree
+BugReports: https://github.com/StochasticTree/stochtree/issues
 Config/testthat/edition: 3

R/random_effects.R

@@ -416,6 +416,18 @@ createRandomEffectsModel <- function(num_components, num_groups) {
 #' rfx_model <- createRandomEffectsModel(num_components, num_groups)
 #' rfx_tracker <- createRandomEffectsTracker(rfx_group_ids)
 #' rfx_samples <- createRandomEffectSamples(num_components, num_groups, rfx_tracker)
+#' alpha_init <- rep(1,num_components)
+#' xi_init <- matrix(rep(alpha_init, num_groups),num_components,num_groups)
+#' sigma_alpha_init <- diag(1,num_components,num_components)
+#' sigma_xi_init <- diag(1,num_components,num_components)
+#' sigma_xi_shape <- 1
+#' sigma_xi_scale <- 1
+#' rfx_model$set_working_parameter(alpha_init)
+#' rfx_model$set_group_parameters(xi_init)
+#' rfx_model$set_working_parameter_cov(sigma_alpha_init)
+#' rfx_model$set_group_parameter_cov(sigma_xi_init)
+#' rfx_model$set_variance_prior_shape(sigma_xi_shape)
+#' rfx_model$set_variance_prior_scale(sigma_xi_scale)
 #' for (i in 1:3) {
 #'     rfx_model$sample_random_effect(rfx_dataset=rfx_dataset, residual=outcome, 
 #'                                    rfx_tracker=rfx_tracker, rfx_samples=rfx_samples, 
@@ -458,6 +470,18 @@ resetRandomEffectsModel <- function(rfx_model, rfx_samples, sample_num, sigma_al
 #' rfx_model <- createRandomEffectsModel(num_components, num_groups)
 #' rfx_tracker <- createRandomEffectsTracker(rfx_group_ids)
 #' rfx_samples <- createRandomEffectSamples(num_components, num_groups, rfx_tracker)
+#' alpha_init <- rep(1,num_components)
+#' xi_init <- matrix(rep(alpha_init, num_groups),num_components,num_groups)
+#' sigma_alpha_init <- diag(1,num_components,num_components)
+#' sigma_xi_init <- diag(1,num_components,num_components)
+#' sigma_xi_shape <- 1
+#' sigma_xi_scale <- 1
+#' rfx_model$set_working_parameter(alpha_init)
+#' rfx_model$set_group_parameters(xi_init)
+#' rfx_model$set_working_parameter_cov(sigma_alpha_init)
+#' rfx_model$set_group_parameter_cov(sigma_xi_init)
+#' rfx_model$set_variance_prior_shape(sigma_xi_shape)
+#' rfx_model$set_variance_prior_scale(sigma_xi_scale)
 #' for (i in 1:3) {
 #'     rfx_model$sample_random_effect(rfx_dataset=rfx_dataset, residual=outcome, 
 #'                                    rfx_tracker=rfx_tracker, rfx_samples=rfx_samples, 
@@ -493,15 +517,21 @@ resetRandomEffectsTracker <- function(rfx_tracker, rfx_model, rfx_dataset, resid
 #' rng <- createCppRNG(1234)
 #' num_groups <- length(unique(rfx_group_ids))
 #' num_components <- ncol(rfx_basis)
-#' alpha_init <- c(1)
+#' rfx_model <- createRandomEffectsModel(num_components, num_groups)
+#' rfx_tracker <- createRandomEffectsTracker(rfx_group_ids)
+#' rfx_samples <- createRandomEffectSamples(num_components, num_groups, rfx_tracker)
+#' alpha_init <- rep(1,num_components)
 #' xi_init <- matrix(rep(alpha_init, num_groups),num_components,num_groups)
 #' sigma_alpha_init <- diag(1,num_components,num_components)
 #' sigma_xi_init <- diag(1,num_components,num_components)
 #' sigma_xi_shape <- 1
 #' sigma_xi_scale <- 1
-#' rfx_model <- createRandomEffectsModel(num_components, num_groups)
-#' rfx_tracker <- createRandomEffectsTracker(rfx_group_ids)
-#' rfx_samples <- createRandomEffectSamples(num_components, num_groups, rfx_tracker)
+#' rfx_model$set_working_parameter(alpha_init)
+#' rfx_model$set_group_parameters(xi_init)
+#' rfx_model$set_working_parameter_cov(sigma_alpha_init)
+#' rfx_model$set_group_parameter_cov(sigma_xi_init)
+#' rfx_model$set_variance_prior_shape(sigma_xi_shape)
+#' rfx_model$set_variance_prior_scale(sigma_xi_scale)
 #' for (i in 1:3) {
 #'     rfx_model$sample_random_effect(rfx_dataset=rfx_dataset, residual=outcome, 
 #'                                    rfx_tracker=rfx_tracker, rfx_samples=rfx_samples, 
@@ -540,15 +570,21 @@ rootResetRandomEffectsModel <- function(rfx_model, alpha_init, xi_init, sigma_al
 #' rng <- createCppRNG(1234)
 #' num_groups <- length(unique(rfx_group_ids))
 #' num_components <- ncol(rfx_basis)
-#' alpha_init <- c(1)
+#' rfx_model <- createRandomEffectsModel(num_components, num_groups)
+#' rfx_tracker <- createRandomEffectsTracker(rfx_group_ids)
+#' rfx_samples <- createRandomEffectSamples(num_components, num_groups, rfx_tracker)
+#' alpha_init <- rep(1,num_components)
 #' xi_init <- matrix(rep(alpha_init, num_groups),num_components,num_groups)
 #' sigma_alpha_init <- diag(1,num_components,num_components)
 #' sigma_xi_init <- diag(1,num_components,num_components)
 #' sigma_xi_shape <- 1
 #' sigma_xi_scale <- 1
-#' rfx_model <- createRandomEffectsModel(num_components, num_groups)
-#' rfx_tracker <- createRandomEffectsTracker(rfx_group_ids)
-#' rfx_samples <- createRandomEffectSamples(num_components, num_groups, rfx_tracker)
+#' rfx_model$set_working_parameter(alpha_init)
+#' rfx_model$set_group_parameters(xi_init)
+#' rfx_model$set_working_parameter_cov(sigma_alpha_init)
+#' rfx_model$set_group_parameter_cov(sigma_xi_init)
+#' rfx_model$set_variance_prior_shape(sigma_xi_shape)
+#' rfx_model$set_variance_prior_scale(sigma_xi_scale)
 #' for (i in 1:3) {
 #'     rfx_model$sample_random_effect(rfx_dataset=rfx_dataset, residual=outcome, 
 #'                                    rfx_tracker=rfx_tracker, rfx_samples=rfx_samples, 

cran-comments.md

@@ -27,3 +27,20 @@ We have converted `T` and `F` to `TRUE` and `FALSE` in the R code.
 > Some code lines in examples are commented out. Please never do that.
 
 We no longer do this, and apologize for the oversight.
+
+## CRAN comments (20250207)
+
+Below we address issues raised by CRAN on Feb 7, 2025
+
+### Valgrind
+
+A valgrind-instrumented version of R exposed memory issues in several examples 
+in the `stochtree` documentation. The specific issue is 
+
+> Conditional jump or move depends on uninitialised value(s)
+
+The examples that triggered this were in fact working with Eigen matrices 
+with uninitialized values. 
+
+This has been corrected and we have verified that running the `stochtree` 
+examples no longer produce this memcheck error.