Prepare 0.10.3 release. (grf-labs#422)

* Bump the version to 0.10.3.
* Update release notes.
* Fix CRAN warnings.
* Apply the fix around passing a data frame as X.
* Add the release tarball.

core/third_party/optional/optional.hpp

@@ -1534,4 +1534,4 @@ struct hash< nonstd::optional<T> >
 
 #endif // optional_USES_STD_OPTIONAL
 
-#endif // NONSTD_OPTIONAL_LITE_HPP
+#endif // NONSTD_OPTIONAL_LITE_HPP

r-package/grf/DESCRIPTION

@@ -1,6 +1,6 @@
 Package: grf
 Title: Generalized Random Forests (Beta)
-Version: 0.10.2
+Version: 0.10.3
 Author: Julie Tibshirani [aut, cre],
     Susan Athey [aut],
     Rina Friedberg [ctb],

r-package/grf/R/boosted_regression_forest.R

@@ -191,6 +191,8 @@ boosted_regression_forest <- function(X, Y,
 #' @param boost.predict.steps Number of boosting iterations to use for prediction. If blank, uses the full number of steps
 #'        for the object given
 #' @param num.threads the number of threads used in prediction
+#' @param ... Additional arguments (currently ignored).
+#'
 #' @return A vector of predictions.
 #'
 #' @examples \dontrun{
@@ -214,7 +216,8 @@ boosted_regression_forest <- function(X, Y,
 #' @export
 predict.boosted_regression_forest <- function(object, newdata=NULL,
                                               boost.predict.steps=NULL,
-                                              num.threads=NULL) {
+                                              num.threads=NULL,
+                                              ...) {
 
   # If not on new data, use pre-computed predictions
   if (is.null(newdata)) {

r-package/grf/R/input_utilities.R

@@ -228,6 +228,7 @@ create_data_matrices <- function(X, ..., sample.weights=NULL) {
   if (inherits(X, "dgCMatrix") && ncol(X) > 1) {
     sparse.data <- cbind(X, ..., sample.weights)
   } else {
+    X <- as.matrix(X)
     default.data <- as.matrix(cbind(X, ..., sample.weights))
   }
 

r-package/grf/R/regression_tuning.R

@@ -10,6 +10,8 @@
 #'
 #' @param X The covariates used in the regression.
 #' @param Y The outcome.
+#' @param sample.weights (experimental) Weights given to an observation in estimation.
+#'                       If NULL, each observation is given the same weight.
 #' @param num.fit.trees The number of trees in each 'mini forest' used to fit the tuning model.
 #' @param num.fit.reps The number of forests used to fit the tuning model.
 #' @param num.optimize.reps The number of random parameter values considered when using the model
@@ -30,7 +32,6 @@
 #' @param samples.per.cluster If sampling by cluster, the number of observations to be sampled from
 #'                            each cluster. Must be less than the size of the smallest cluster. If set to NULL
 #'                            software will set this value to the size of the smallest cluster.
-#' @param compute.oob.predictions Whether OOB predictions on training set should be precomputed.
 #' @param num.threads Number of threads used in training. By default, the number of threads is set
 #'                    to the maximum hardware concurrency.
 #' @param seed The seed of the C++ random number generator.

releases/CHANGELOG.md

@@ -1,15 +1,37 @@
 # Changelog
-All notable changes to this project will be documented in this file.
+All notable changes to grf will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
+## [0.10.3] - 2019-05-03
+
+### Breaking Changes
+- Fix two bugs in the termination criterion for tree splitting. **IMPORTANT:** these bug fixes may cause results to change compared to previous releases, even if the same random seed is used.
+  - Remove the purity condition on outcomes during splitting. For all tree types, we used to stop splitting if all outcomes in a leaf are the same. This behavior does not make sense for causal forests (which incorporates other observations besides the outcome), so it was removed. [#362](https://github.com/grf-labs/grf/pull/362).
+  - Stop splitting if the objective can no longer be improved. With this change, `causal_forest` may split slightly less aggressively. [#415](https://github.com/grf-labs/grf/pull/415)
+
+### Added
+- In out-of-bag prediction, return the Monte Carlo error alongside the debiased error. [#327](https://github.com/grf-labs/grf/pull/327)
+- Allow for passing a factor for the `cluster` parameter. [#329](https://github.com/grf-labs/grf/pull/329)
+- Support taking a union of forests through the `merge_forests` method. [#347](https://github.com/grf-labs/grf/pull/347)
+- Include a summary of the parameter tuning procedure in the forest object. [#419] (https://github.com/grf-labs/grf/pull/419)
+- Add experimental support for sample weighting to regression, causal, and instrumental forests. [#376](https://github.com/grf-labs/grf/pull/376), [#418](https://github.com/grf-labs/grf/pull/418)
+- Add a new forest type `boosted_regression_forest`, which applies boosting to regression forests. Allow boosting to be used during orthogonalization through the `orthog.boosting` parameter. [#388](https://github.com/grf-labs/grf/pull/388)
+
+### Fixed
+- Improve input data validation. [#354](https://github.com/grf-labs/grf/pull/354), [#378](https://github.com/grf-labs/grf/pull/378), [#430](https://github.com/grf-labs/grf/pull/430)
+- Improve the `test_calibration` function by switching to one-sided p-values. [#370](https://github.com/grf-labs/grf/pull/370)
+- For custom forests, fix a bug in OOB prediction where the train and tests datasets were switched. [#372](https://github.com/grf-labs/grf/pull/372)
+- Decrease memory usage during training and out-of-bag prediction. [#408](https://github.com/grf-labs/grf/pull/408), [#412](https://github.com/grf-labs/grf/pull/412)
+- Allow roxygen to autogenerate the `NAMESPACE` file. [#423](https://github.com/grf-labs/grf/pull/423), [#428](https://github.com/grf-labs/grf/pull/428)
+
 ## [0.10.2] - 2018-11-23
 ### Added
-- Add support for confidence intervals in local linear regression forests. 
+- Add support for confidence intervals in local linear regression forests.
 
 ### Changed
-- Allow samples_per_cluster to be larger than smallest cluster size. 
+- Allow samples_per_cluster to be larger than smallest cluster size.
 
 ### Fixed
 - Make sure average effect estimation doesn't error on data with a single feature.
@@ -81,7 +103,7 @@ causing poor performance for even moderately large numbers of features.
 ## [0.9.4] - 2017-11-25
 
 ### Changed
-- Update the default for mtry to sqrt(p) + 20. 
+- Update the default for mtry to sqrt(p) + 20.
 
 ### Fixed
 - Fix an issue where split_frequencies fails when p = 1.