Merge pull request #157 from NSAPH-Software/release_ver0.2.7

Release ver0.2.7

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

@@ -22,7 +22,7 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - {os: windows-latest, r: '4.2'}
+          #- {os: windows-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" }
@@ -88,6 +88,7 @@ jobs:
       - name: Install dependencies
         run: |
           remotes::install_deps(dependencies = TRUE)
+          remotes::install_cran("curl")
           remotes::install_cran("rcmdcheck")
           remotes::install_cran("devtools")
           remotes::install_cran("RcppParallel")

DESCRIPTION

@@ -1,20 +1,20 @@
 Type: Package
 Package: CRE
 Title: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
-Version: 0.2.6
+Version: 0.2.7
 Authors@R: c(
-    person("Naeem", "Khoshnevis", , "[email protected]", role = c("aut", "cre"),
-           comment = c(ORCID = "0000-0003-4315-1426", AFFILIATION = "FASRC")),
+    person("Naeem", "Khoshnevis", , "[email protected]", role = "aut",
+           comment = c(ORCID = "0000-0003-4315-1426")),
     person("Daniela Maria", "Garcia", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0003-3226-3561")),
     person("Riccardo", "Cadei", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0003-2416-8943")),
     person("Kwonsang", "Lee", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0002-5823-4331")),
-    person("Falco Joannes", "Bargagli Stoffi", , "[email protected]", role = "aut",
+    person("Falco Joannes", "Bargagli Stoffi", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-6131-8165"))
   )
-Maintainer: Naeem Khoshnevis <nkhoshnevis@g.harvard.edu>
+Maintainer: Falco Joannes Bargagli Stoffi <fbargaglistoffi@hsph.harvard.edu>
 Description: Provides a new method for interpretable heterogeneous 
     treatment effects characterization in terms of decision rules 
     via an extensive exploration of heterogeneity patterns by an 
@@ -63,4 +63,4 @@ Encoding: UTF-8
 Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2

NEWS.md

@@ -1,4 +1,9 @@
-# CRE 0.2.5 (2024-4-21)
+# CRE 0.2.7 (2024-10-19)
+
+## Changed
+*  The maintainer role has been transitioned from Dr. Naeem Khoshnevis to Dr. Falco Joannes Bargagli Stoffi.
+
+# CRE 0.2.6 (2024-4-21)
 
 ## Added
 * A copy of inTrees package source code.

R/CRE_package.R

@@ -20,7 +20,6 @@
 #' a general two-stage approach for subgroup-specific conditional
 #' causal effects estimation, providing theoretical guarantees.
 #'
-#' @docType package
 #' @name CRE-package
 #' @aliases CRE
 #' @author Naeem Khoshnevis
@@ -43,4 +42,4 @@
 #' Causal rule ensemble: Interpretable Discovery and Inference of
 #' Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036
 #'
-NULL
+"_PACKAGE"

README.md

@@ -69,8 +69,8 @@ __Parameters (not required)__
 **`method_parameters`** The list of parameters to define the models used, including:
 - **`ratio_dis`** The ratio of data delegated to the discovery sub-sample (default: 0.5). 
 - **`ite_method`** The method to estimate the individual treatment effect (ITE) pseudo-outcome estimation (default: "aipw") [1].        
-- **`learner_ps`** The ([SuperLearner](https://CRAN.R-project.org/package=SuperLearner)) model for the propensity score estimation (default: "SL.xgboost", used only for "aipw","bart","cf" ITE estimators).
-- **`learner_y`** The ([SuperLearner](https://CRAN.R-project.org/package=SuperLearner)) model for the outcome estimation (default: "SL.xgboost", used only for "aipw","slearner","tlearner" and "xlearner" ITE estimators).   
+- **`learner_ps`** The SuperLearner model for the propensity score estimation (default: "SL.xgboost", used only for "aipw","bart","cf" ITE estimators).
+- **`learner_y`** The SuperLearner model for the outcome estimation (default: "SL.xgboost", used only for "aipw","slearner","tlearner" and "xlearner" ITE estimators).   
 
 **`hyper_params`** The list of hyper parameters to finetune the method, including:
 - **`intervention_vars`** Array with intervention-able covariates names used for Rules Generation. Empty or null array means that all the covariates are considered as intervention-able (default: `NULL`).  
@@ -95,15 +95,15 @@ __Additional Estimates (not required)__
 ## Notes
 
 **[1]** Options for the ITE estimation are as follows: 
-- [S-Learner](https://CRAN.R-project.org/package=SuperLearner) (`slearner`)
-- [T-Learner](https://CRAN.R-project.org/package=SuperLearner) (`tlearner`)
+- S-Learner (`slearner`)
+- T-Learner (`tlearner`)
 - T-Poisson (`tpoisson`)
-- [X-Learner](https://CRAN.R-project.org/package=SuperLearner) (`xlearner`)
-- [Augmented Inverse Probability Weighting](https://CRAN.R-project.org/package=SuperLearner) (`aipw`)
-- [Causal Forests](https://CRAN.R-project.org/package=grf) (`cf`)
-- [Causal Bayesian Additive Regression Trees](https://CRAN.R-project.org/package=bartCause) (`bart`)
+- X-Learner (`xlearner`)
+- Augmented Inverse Probability Weighting (`aipw`)
+- Causal Forests (`cf`)
+- Causal Bayesian Additive Regression Trees (`bart`)
 
-If other estimates of the ITE are provided in `ite` additional argument, both the ITE estimations in discovery and inference are skipped and those values estimates are used instead.  The ITE estimator requires also an outcome learner and/or a propensity score learner from the [SuperLearner](https://CRAN.R-project.org/package=SuperLearner) package (i.e., "SL.lm", "SL.svm"). Both these models are simple classifiers/regressors. By default XGBoost algorithm is used for both these steps.
+If other estimates of the ITE are provided in `ite` additional argument, both the ITE estimations in discovery and inference are skipped and those values estimates are used instead.  The ITE estimator requires also an outcome learner and/or a propensity score learner from the SuperLearner package (i.e., "SL.lm", "SL.svm"). Both these models are simple classifiers/regressors. By default XGBoost algorithm is used for both these steps.
 
 
 ## Examples

_pkgdown.yml

@@ -29,10 +29,6 @@ reference:
     - get_logger
 
 articles:
-  - title: Users
-    navbar: Users
-    contents: []
-
   - title: Developers
     navbar: Developers
     contents: [Contribution, Testing-the-Package]

cran-comments.md

@@ -1,26 +1,25 @@
-Resubmission (April 21, 2024):
+Resubmission (October 17, 2024):
 
-We appreciate your efforts in reviewing the CRE 0.2.6 package. In this upgrade, we:
+We would like to express our gratitude for your efforts in reviewing the CRE 0.2.7 package. Based on Kurt Hornik's recommendations, we have removed the affiliation from the Author field and rebuilt the package using the current version of R.
 
-- Fixed the issue with the arXiv paper format in the DESCRIPTION file. Specifically, we changed ":" to ".". 
+Additionally, please note that with this update, the maintainer role has been transitioned from Dr. Naeem Khoshnevis to Dr. Falco Joannes Bargagli Stoffi. All tests and checks have been completed successfully.
 
 Best regards, 
-Naeem Khoshnevis
+Falco Joannes Bargagli Stoffi
 
-Resubmission (April 21, 2024):
 
-We appreciate your efforts in reviewing the CRE 0.2.6 package. In this upgrade, we:
+Resubmission (September 12, 2024):
 
-- Fixed the issue with the arXiv paper format in the DESCRIPTION file. 
+We appreciate your efforts in reviewing the CRE 0.2.7 package. We fixed the issues with URL in the README.md file. Please note that in this upgrade we transfer the maintainer role from Dr. Naeem Khoshnevis to Dr. Falco Joannes Bargagli Stoffi. All tests and checks have passed successfully.
 
 Best regards, 
-Naeem Khoshnevis
+Falco Joannes Bargagli Stoffi
 
-Submission (April 21, 2024):
+Submission (September 10, 2024):
 
-We appreciate your efforts in reviewing the CRE 0.2.6 package. In this upgrade, we:
+We appreciate your efforts in reviewing the CRE 0.2.7 package. In this upgrade, we:
 
-- Dropped the dependency on the inTrees R package, which is set to be archived on May 4. 
+Transfer the maintainer role from Dr. Naeem Khoshnevis to Dr. Falco Joannes Bargagli Stoffi. All tests and checks have passed successfully.
 
 Best regards, 
-Naeem Khoshnevis
+Falco Joannes Bargagli Stoffi