Merge pull request #156 from NSAPH-Software/release_v0.2.6

Release v0.2.6

Author: Naeemkh

.covrignore

@@ -3,3 +3,4 @@ R/plot.R
 R/print.R
 R/check_hyper_params.R
 R/logger_utils.R
+R/inTrees.R

.github/workflows/R-CMD-check.yaml

@@ -23,7 +23,7 @@ jobs:
       matrix:
         config:
           - {os: windows-latest, r: '4.2'}
-          # - {os: macOS-latest, r: '4.2'}
+          #- {os: macOS-latest, r: '4.2'}
           - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
           - {os: ubuntu-20.04,   r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest", http-user-agent: "R/4.1.0 (ubuntu-20.04) R (4.1.0 x86_64-pc-linux-gnu x86_64 linux-gnu) on GitHub Actions" }
 

.github/workflows/draft-pdf.yml

@@ -1,7 +1,7 @@
 Type: Package
 Package: CRE
 Title: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
-Version: 0.2.5.9000
+Version: 0.2.6
 Authors@R: c(
     person("Naeem", "Khoshnevis", , "nkhoshnevis@g.harvard.edu", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-4315-1426", AFFILIATION = "FASRC")),
@@ -23,7 +23,7 @@ Description: Provides a new method for interpretable heterogeneous
     it is supported by theoretical convergence guarantees. Bargagli-Stoffi, 
     F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: 
     Interpretable Discovery and Inference of Heterogeneous Treatment Effects.  
-    arXiv preprint <arXiv:2009.09036>.
+    arXiv preprint <doi:10.48550/arXiv.2009.09036>. 
 License: GPL-3
 URL: https://github.com/NSAPH-Software/CRE
 BugReports: https://github.com/NSAPH-Software/CRE/issues
@@ -47,7 +47,7 @@ Imports:
     SuperLearner,
     magrittr,
     ggplot2,
-    inTrees
+    arules
 Suggests:
     grf,
     BART,

DESCRIPTION

@@ -1,3 +1,11 @@
+# CRE 0.2.5 (2024-4-21)
+
+## Added
+* A copy of inTrees package source code.
+
+## Removed
+* The inTrees package dependency
+
 # CRE 0.2.5 (2023-12-6)
 
 ## Added

NEWS.md

@@ -31,7 +31,7 @@ extract_rules <- function(treelist, X, max_depth, digits = 2) {
     if (nrow(tree) <= 1) next # skip if there is no split
     ruleSet <- vector("list", length(which(tree[, "status"] == -1)))
     for (max_length in 1:max_depth) {
-      res <- inTrees::treeVisit(tree,
+      res <- inTrees_treeVisit(tree,
                                 rowIx = rowIx,
                                 count,
                                 ruleSet,
@@ -45,6 +45,6 @@ extract_rules <- function(treelist, X, max_depth, digits = 2) {
   }
 
   allRulesList <- allRulesList[!unlist(lapply(allRulesList, is.null))]
-  rules <- inTrees::ruleList2Exec(X, allRulesList)
+  rules <- inTrees_ruleList2Exec(X, allRulesList)
   return(rules)
 }

R/extract_rules.R

@@ -24,11 +24,11 @@ filter_irrelevant_rules <- function(rules, X, ite, t_decay) {
 
   rules_matrix <- matrix(rules)
   colnames(rules_matrix) <- "condition"
-  metric <- inTrees::getRuleMetric(rules_matrix,
+  metric <- inTrees_getRuleMetric(rules_matrix,
                                    X,
                                    ite_)
 
-  pruned <- inTrees::pruneRule(rules = metric,
+  pruned <- inTrees_pruneRule(rules = metric,
                                X = X,
                                target = ite_,
                                maxDecay = t_decay)

R/filter_irrelevant_rules.R

@@ -34,7 +34,7 @@ generate_rules <- function(X, ite, ntrees, node_size, max_rules, max_depth) {
                                          nodesize = node_size,
                                          mtry = ncol(X)*2/3)
 
-    treelist <- inTrees::RF2List(forest)
+    treelist <- inTrees_RF2List(forest)
     rules <- extract_rules(treelist, X, max_depth)
   } else {
     rules <- NULL