Prepare the 1.1.0 release (grf-labs#634)

README.md

@@ -27,7 +27,7 @@ install.packages("grf")
 Any published release can also be installed from source:
 
 ```R
-install.packages("https://raw.github.com/grf-labs/grf/master/releases/grf_1.0.1.tar.gz", repos = NULL, type = "source")
+install.packages("https://raw.github.com/grf-labs/grf/master/releases/grf_1.1.0.tar.gz", repos = NULL, type = "source")
 ```
 
 `conda` users can install from the [conda-forge](https://anaconda.org/conda-forge/r-grf) channel:

r-package/grf/DESCRIPTION

@@ -1,6 +1,6 @@
 Package: grf
 Title: Generalized Random Forests
-Version: 1.0.1
+Version: 1.1.0
 Authors@R: c(
     person("Julie", "Tibshirani", role = c("aut", "cre"), email = "[email protected]"),
     person("Susan", "Athey", role = "aut"),

r-package/grf/R/local_linear_forest.R

@@ -5,7 +5,7 @@
 #'
 #' @param X The covariates used in the regression.
 #' @param Y The outcome.
-#' @param enable.ll.split (experimental) Optional choice to make forest splits based on ridge residuals as opposed to 
+#' @param enable.ll.split (experimental) Optional choice to make forest splits based on ridge residuals as opposed to
 #'                        standard CART splits. Defaults to FALSE.
 #' @param ll.split.weight.penalty If using local linear splits, user can specify whether or not to use a
 #'                                covariance ridge penalty, analogously to the prediction case. Defaults to FALSE.
@@ -18,6 +18,8 @@
 #' @param num.trees Number of trees grown in the forest. Note: Getting accurate
 #'                  confidence intervals generally requires more trees than
 #'                  getting accurate predictions. Default is 2000.
+#' @param sample.weights (experimental) Weights given to an observation in estimation.
+#'                       If NULL, each observation is given the same weight. Default is NULL.
 #' @param clusters Vector of integers or factors specifying which cluster each observation corresponds to.
 #'  Default is NULL (ignored).
 #' @param equalize.cluster.weights If FALSE, each unit is given the same weight (so that bigger

releases/CHANGELOG.md

@@ -4,6 +4,23 @@ 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).
 
+## [1.1.0] - 2020-03-12
+
+### Changed (breaking)
+**IMPORTANT** These changes might cause small differences in results compared to previous releases, even if the same random seed is used.
+- Performance improvement: remove an unnecessary splitting rule loop. Note: this may cause very small differences from earlier versions because it changes the order in which potential splits are evaluated. [#592](https://github.com/grf-labs/grf/pull/592)
+
+### Added
+- Add support for missing values in the covariates X with [MIA](https://github.com/grf-labs/grf/issues/457) splitting. [#612](https://github.com/grf-labs/grf/pull/612)
+- Add local linear splitting. An experimental option `enable.ll.split` fits a forest with splits based on ridge residuals as opposed to standard CART splits. Note: local linear tuning does not take the new splits into account. [#603](https://github.com/grf-labs/grf/pull/603)
+- Add sample weighted splitting. Previously, if a user passed `sample.weights`, they would only be used for prediction. Now they are used in splitting as well. Note: this will make results fitted with sample weights different from previous versions. [#590](https://github.com/grf-labs/grf/pull/590)
+
+### Fixed
+- Remove a superfluous predict call in tuning. [#597](https://github.com/grf-labs/grf/pull/597)
+- Fix `average_partial_effect` calibration in case of low variation W.hat. [#611](https://github.com/grf-labs/grf/pull/611)
+- Update `best_linear_projection` to handle non-binary treatment. [#615](https://github.com/grf-labs/grf/pull/615)
+- Add an error message in case summary functions are passed a subset that refers to too few distinct units. [#629](https://github.com/grf-labs/grf/pull/629)
+
 ## [1.0.1] - 2019-12-05
 
 ### Fixed