Merge pull request #132 from StochasticTree/initial-cran-release-prep

Initial CRAN release preparation

Author: andrewherren

.Rbuildignore

@@ -1,2 +1,3 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+^cran-comments\.md$

.github/workflows/r-test.yml

@@ -39,7 +39,7 @@ jobs:
 
       - name: Create a CRAN-ready version of the R package
         run: |
-          Rscript cran-bootstrap.R 0
+          Rscript cran-bootstrap.R 0 0
       
       - uses: r-lib/actions/check-r-package@v2
         with:

DESCRIPTION

@@ -1,13 +1,14 @@
 Package: stochtree
 Title: Stochastic tree ensembles (XBART and BART) for supervised learning and causal inference
-Version: 0.0.1
+Version: 0.1.0
 Authors@R: 
   c(
     person("Drew", "Herren", email = "drewherrenopensource@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4109-6611")), 
     person("Richard", "Hahn", role = "aut"),
     person("Jared", "Murray", role = "aut"),
     person("Carlos", "Carvalho", role = "aut"),
-    person("Jingyu", "He", role = "aut")
+    person("Jingyu", "He", role = "aut"),
+    person("stochtree contributors", role = c("cph"))
   )
 Description: Stochastic tree ensembles (XBART and BART) for supervised learning and causal inference.
 License: MIT + file LICENSE

NAMESPACE

@@ -1,40 +1,37 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(getRandomEffectSamples,bartmodel)
-S3method(getRandomEffectSamples,bcf)
+S3method(getRandomEffectSamples,bcfmodel)
 S3method(predict,bartmodel)
-S3method(predict,bcf)
+S3method(predict,bcfmodel)
 export(bart)
 export(bcf)
-export(calibrate_inverse_gamma_error_variance)
+export(calibrateInverseGammaErrorVariance)
 export(computeForestLeafIndices)
 export(computeForestLeafVariances)
-export(computeMaxLeafIndex)
-export(convertBARTModelToJson)
-export(convertBCFModelToJson)
+export(computeForestMaxLeafIndex)
 export(convertPreprocessorToJson)
 export(createBARTModelFromCombinedJson)
 export(createBARTModelFromCombinedJsonString)
 export(createBARTModelFromJson)
 export(createBARTModelFromJsonFile)
 export(createBARTModelFromJsonString)
+export(createBCFModelFromCombinedJson)
 export(createBCFModelFromCombinedJsonString)
 export(createBCFModelFromJson)
 export(createBCFModelFromJsonFile)
 export(createBCFModelFromJsonString)
 export(createCppJson)
 export(createCppJsonFile)
 export(createCppJsonString)
+export(createCppRNG)
 export(createForest)
-export(createForestContainer)
-export(createForestCovariates)
-export(createForestCovariatesFromMetadata)
 export(createForestDataset)
 export(createForestModel)
+export(createForestSamples)
 export(createOutcome)
 export(createPreprocessorFromJson)
 export(createPreprocessorFromJsonString)
-export(createRNG)
 export(createRandomEffectSamples)
 export(createRandomEffectsDataset)
 export(createRandomEffectsModel)
@@ -48,35 +45,28 @@ export(loadRandomEffectSamplesCombinedJsonString)
 export(loadRandomEffectSamplesJson)
 export(loadScalarJson)
 export(loadVectorJson)
-export(oneHotEncode)
-export(oneHotInitializeAndEncode)
-export(orderedCatInitializeAndPreprocess)
-export(orderedCatPreprocess)
-export(preprocessParams)
 export(preprocessPredictionData)
-export(preprocessPredictionDataFrame)
-export(preprocessPredictionMatrix)
 export(preprocessTrainData)
-export(preprocessTrainDataFrame)
-export(preprocessTrainMatrix)
 export(resetActiveForest)
 export(resetForestModel)
 export(resetRandomEffectsModel)
 export(resetRandomEffectsTracker)
-export(rootResetActiveForest)
 export(rootResetRandomEffectsModel)
 export(rootResetRandomEffectsTracker)
-export(sample_sigma2_one_iteration)
-export(sample_tau_one_iteration)
+export(sampleGlobalErrorVarianceOneIteration)
+export(sampleLeafVarianceOneIteration)
+export(saveBARTModelToJson)
 export(saveBARTModelToJsonFile)
 export(saveBARTModelToJsonString)
+export(saveBCFModelToJson)
 export(saveBCFModelToJsonFile)
 export(saveBCFModelToJsonString)
 export(savePreprocessorToJsonString)
 importFrom(R6,R6Class)
 importFrom(stats,coef)
 importFrom(stats,lm)
 importFrom(stats,model.matrix)
+importFrom(stats,predict)
 importFrom(stats,qgamma)
 importFrom(stats,resid)
 importFrom(stats,rnorm)

NEWS.md

@@ -0,0 +1,3 @@
+# stochtree 0.1.0
+
+* Initial CRAN submission.

R/bart.R

@@ -18,10 +18,10 @@
 #' X <- matrix(runif(n*p), ncol = p)
 #' y <- 10*X[,1] - 20*X[,2] + rnorm(n)
 #' nu <- 3
-#' lambda <- calibrate_inverse_gamma_error_variance(y, X, nu = nu)
+#' lambda <- calibrateInverseGammaErrorVariance(y, X, nu = nu)
 #' sigma2hat <- mean(resid(lm(y~X))^2)
 #' mean(var(y)/rgamma(100000, nu, rate = nu*lambda) < sigma2hat)
-calibrate_inverse_gamma_error_variance <- function(y, X, W = NULL, nu = 3, quant = 0.9, standardize = TRUE) {
+calibrateInverseGammaErrorVariance <- function(y, X, W = NULL, nu = 3, quant = 0.9, standardize = TRUE) {
     # Compute regression basis
     if (!is.null(W)) basis <- cbind(X, W)
     else basis <- X

R/bcf.R

@@ -292,8 +292,8 @@ json_load_forest_container_cpp <- function(forest_samples, json_filename) {
   invisible(.Call(`_stochtree_json_load_forest_container_cpp`, forest_samples, json_filename))
 }
 
-output_dimension_forest_container_cpp <- function(forest_samples) {
-  .Call(`_stochtree_output_dimension_forest_container_cpp`, forest_samples)
+leaf_dimension_forest_container_cpp <- function(forest_samples) {
+  .Call(`_stochtree_leaf_dimension_forest_container_cpp`, forest_samples)
 }
 
 is_leaf_constant_forest_container_cpp <- function(forest_samples) {
@@ -464,8 +464,8 @@ predict_raw_active_forest_cpp <- function(active_forest, dataset) {
   .Call(`_stochtree_predict_raw_active_forest_cpp`, active_forest, dataset)
 }
 
-output_dimension_active_forest_cpp <- function(active_forest) {
-  .Call(`_stochtree_output_dimension_active_forest_cpp`, active_forest)
+leaf_dimension_active_forest_cpp <- function(active_forest) {
+  .Call(`_stochtree_leaf_dimension_active_forest_cpp`, active_forest)
 }
 
 average_max_depth_active_forest_cpp <- function(active_forest) {

R/calibration.R

@@ -228,6 +228,14 @@ RandomEffectsDataset <- R6::R6Class(
 #'
 #' @return `ForestDataset` object
 #' @export
+#' 
+#' @examples
+#' covariate_matrix <- matrix(runif(10*100), ncol = 10)
+#' basis_matrix <- matrix(rnorm(3*100), ncol = 3)
+#' weight_vector <- rnorm(100)
+#' forest_dataset <- createForestDataset(covariate_matrix)
+#' forest_dataset <- createForestDataset(covariate_matrix, basis_matrix)
+#' forest_dataset <- createForestDataset(covariate_matrix, basis_matrix, weight_vector)
 createForestDataset <- function(covariates, basis=NULL, variance_weights=NULL){
     return(invisible((
         ForestDataset$new(covariates, basis, variance_weights)
@@ -240,6 +248,11 @@ createForestDataset <- function(covariates, basis=NULL, variance_weights=NULL){
 #'
 #' @return `Outcome` object
 #' @export
+#' 
+#' @examples
+#' X <- matrix(runif(10*100), ncol = 10)
+#' y <- -5 + 10*(X[,1] > 0.5) + rnorm(100)
+#' outcome <- createOutcome(y)
 createOutcome <- function(outcome){
     return(invisible((
         Outcome$new(outcome)
@@ -254,6 +267,13 @@ createOutcome <- function(outcome){
 #'
 #' @return `RandomEffectsDataset` object
 #' @export
+#' 
+#' @examples
+#' rfx_group_ids <- sample(1:2, size = 100, replace = TRUE)
+#' rfx_basis <- matrix(rnorm(3*100), ncol = 3)
+#' weight_vector <- rnorm(100)
+#' rfx_dataset <- createRandomEffectsDataset(rfx_group_ids, rfx_basis)
+#' rfx_dataset <- createRandomEffectsDataset(rfx_group_ids, rfx_basis, weight_vector)
 createRandomEffectsDataset <- function(group_labels, basis, variance_weights=NULL){
     return(invisible((
         RandomEffectsDataset$new(group_labels, basis, variance_weights)