update

DESCRIPTION

@@ -8,8 +8,7 @@ Depends: R (>= 3.4.0)
 Encoding: UTF-8
 License: GPL-3
 URL: https://github.com/wjiang94/misaem.git
-Imports: magrittr,
-    mvtnorm,
+Imports: mvtnorm,
     stats,
     MASS
 Suggests: knitr,

NEWS.md

@@ -5,3 +5,9 @@ A minor release mainly fixing bugs and typos:
 * Fix a bug in `model_selection`, now it can correctly perform model selection if the full model is the best model.
 
 * Fix some typos in the vignettes. The length of codes now fits the page wide of html.
+
+* Delete unused Imports ‘magrittr’ in DESCRIPTION file.
+
+* Change the index of vignitte from 'SAEM' to 'misaem tutorial'.
+
+* Update README.md.

README.md

@@ -1,19 +1,27 @@
 # misaem
-### Package R for "Stochastic Approximation EM for Logistic Regression with Missing Values (W. Jiang, J. Josse, M. Lavielle, Traumabase Group, 2018)"
 
-`misaem` is a method to apply statistical inference for logistic regression model with missing data. This method is based on likelihood, including 
-1. A stochastic approximation version of EM algorithm based on Metropolis-Hasting sampling, to estimate the parameters of logistic regression;
+### Introduction
+
+`misaem` is an implementation of methodology which performs statistical inference for logistic regression model with missing data. This method is based on likelihood, including:
+
+1. Estimate the parameters of logistic regression by a stochastic approximation version of EM algorithm;
 2. Estimation of parameters' variance based one Louis formula;
-3. Model selection procedure based on BIC.
+3. Model selection procedure based on BIC;
+4. Prediction on a test set which may contain missing values.
 
 ### Installation of package 
-First you can install the package **misaem** from Github. The main function `miss.saem` contains the procedure of estimation for parameters, as well as their variance, and observed likelihood.
+Now you can install the package **misaem** from CRAN. 
 ```{r}
-library(devtools)
-install_github("wjiang94/misaem")
+install.packages("misaem")
  ```
 ### Using the misaem package
-You can find the vignette, which illustrate the basic and further usage of misaem package:
+Basicly,
+
+1. `miss.saem` contains the procedure of estimation for parameters, as well as their variance, and observed likelihood.
+2. `model_selection` aims at selecting a best model according to BIC.
+3. `pred_saem` performs prediction on a test set which may contain missing values.
+
+For more details, You can find the vignette, which illustrate the basic and further usage of misaem package:
 ```{r}
 library(misaem)
 vignette('misaem')

vignettes/misaem.Rmd

@@ -4,7 +4,7 @@ author: "Wei Jiang"
 date: "`r Sys.Date()`"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{SAEM}
+  %\VignetteIndexEntry{misaem tutorial}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---