Update docs

DESCRIPTION

@@ -1,27 +1,27 @@
 Package: RfEmpImp
 Type: Package
 Title: Multiple Imputation using Chained Random Forests
-Version: 2.1.5
+Version: 2.1.6
 Authors@R: c(person("Shangzhi", "Hong", role = c("aut", "cre"),
             email = "[email protected]"),
             person("Henry S.", "Lynn", role = c("ths")))
 Maintainer: Shangzhi Hong <[email protected]>
-Description: An R package for methods of multiple imputation using chained
-    random forests. Implemented methods can handle missing data in mixed types
-    of by using prediction-based or node-based conditional distributions
-    constructed using random forests. For prediction-based imputation,
-    the method based on the empirical distribution of out-of-bag prediction
-    errors of random forests, and the method based on normality assumption are
-    provided for continuous variables. And the method based on predicted
-    probabilities is provided for categorical variables. For node-based
-    imputation, the method based on the conditional distribution formed by
-    the predicting nodes of random forests, and the method based on proximity
-    measures of random forests are provided. More details of the statistical
-    methods can be found in Hong et al. (2020) <arXiv:2004.14823>.
+Description: An R package for multiple imputation using chained random forests.
+    Implemented methods can handle missing data in mixed types of variables by
+    using prediction-based or node-based conditional distributions constructed
+    using random forests. For prediction-based imputation, the method based on
+    the empirical distribution of out-of-bag prediction errors of random forests
+    and the method based on normality assumption for prediction errors of random
+    forests are provided for imputing continuous variables. And the method based
+    on predicted probabilities is provided for imputing categorical variables.
+    For node-based imputation, the method based on the conditional distribution
+    formed by the predicting nodes of random forests, and the method based on
+    proximity measures of random forests are provided. More details of the
+    statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.
 License: GPL-3
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.1.0
+RoxygenNote: 7.1.1
 Depends:
     R (>= 3.5.0),
     mice (>= 3.9.0),

NEWS.md

@@ -1,3 +1,6 @@
+## RfEmpImp 2.1.6
+* Update documentations
+
 ## RfEmpImp 2.1.5
 * Add new wrapper function to recover dependency on `ranger (>= 0.12.1)` and
 to fix parameter passing

R/reg.ests.R

@@ -3,11 +3,12 @@
 #' @description
 #' Get the estimates with corresponding confidence intervals after pooling.
 #'
-#' @param obj Pooled object.
+#' @param obj Pooled object from function \code{pool()}.
 #'
 #' @param ... Other parameters to pass down.
 #'
-#' @return A data frame containing estimates and confidence intervals.
+#' @return A data frame containing coefficient estimates and corresponding
+#' confidence intervals.
 #'
 #' @export
 reg.ests <- function(obj, ...) {

README.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 # RfEmpImp <a href='https://github.com/shangzhi-hong/RfEmpImp'><img src='man/figures/logo.png' align="right" height="160"/></a>
 
 [![CRAN Status Badge](http://www.r-pkg.org/badges/version/RfEmpImp)](https://CRAN.R-project.org/package=RfEmpImp)
-[![GitHub Version Badge](https://img.shields.io/static/v1?label=GitHub&message=2.1.5&color=3399ff)](https://github.com/shangzhi-hong/RfEmpImp)
+[![GitHub Version Badge](https://img.shields.io/static/v1?label=GitHub&message=2.1.6&color=3399ff)](https://github.com/shangzhi-hong/RfEmpImp)
 [![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)  
 An R package for random-forest-empowered imputation of missing Data
 

README.md

@@ -6,7 +6,7 @@
 [![CRAN Status
 Badge](http://www.r-pkg.org/badges/version/RfEmpImp)](https://CRAN.R-project.org/package=RfEmpImp)
 [![GitHub Version
-Badge](https://img.shields.io/static/v1?label=GitHub&message=2.1.5&color=3399ff)](https://github.com/shangzhi-hong/RfEmpImp)
+Badge](https://img.shields.io/static/v1?label=GitHub&message=2.1.6&color=3399ff)](https://github.com/shangzhi-hong/RfEmpImp)
 [![Lifecycle:
 maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)  
 An R package for random-forest-empowered imputation of missing Data