update README.rmd

CRAN-RELEASE

@@ -1,2 +1,2 @@
-This package was submitted to CRAN on 2020-09-25.
-Once it is accepted, delete this file and tag the release (commit 4ff41b6).
+This package was submitted to CRAN on 2020-11-02.
+Once it is accepted, delete this file and tag the release (commit ecfae4d).

README.Rmd

@@ -25,15 +25,15 @@ knitr::opts_chunk$set(
 **rMIDAS** is an R package for multiply imputing missing data using an accurate and efficient algorithm based on deep learning methods. The package provides a simplified workflow for imputing and then analyzing data:
 
 * `convert()` carries out all necessary preprocessing steps
-* `train()` constructs and trains a MIDAS imputation model.
+* `train()` constructs and trains a MIDAS imputation model
 * `complete()` generates multiple completed datasets from the trained model
-* `combine()` runs regression analysis across the complete data, following Rubin's Rules.
+* `combine()` runs regression analysis across the complete data, following Rubin's combination Rules
 
 **rMIDAS** is based on the Python class [MIDASpy](https://github.com/MIDASverse/MIDASpy).
 
 ### Efficient handling of large data
 
-**rMIDAS** also incorporates several features to streamline and improve the efficiency of multiple imputation analysis: 
+rMIDAS also incorporates several features to streamline and improve the the efficiency of multiple imputation analysis:
 
 * Optimisation for large datasets using `data.table` and `mltools` packages
 * Automatic reversing of all pre-processing steps prior to analysis
@@ -47,7 +47,7 @@ Lall, Ranjit, and Thomas Robinson. 2020. “Applying the MIDAS Touch: How to Han
 
 ## Installation
 
-**rMIDAS** is now available on [CRAN](https://cran.r-project.org/package=rMIDAS). To install the package in R, you can use the following code:
+rMIDAS is now available on [CRAN](https://cran.r-project.org/package=rMIDAS). To install the package in R, you can use the following code:
 
 ```{r, eval = FALSE}
 install.packages("rMIDAS")
@@ -60,7 +60,7 @@ To install the latest development version, please use the following code:
 devtools::install_github("MIDASverse/rMIDAS")
 ```
 
-Note that **rMIDAS** uses the [reticulate](https://github.com/rstudio/reticulate) package to interface with Python. Users must have Python 3.5 - 3.8 installed in order to run MIDAS (Python 3.9 is not yet supported). rMIDAS will automatically try to set up a Python configuration unless users specify their own version using `set_python_env()` (examples below). Setting a custom Python install must be performed *before* training or imputing data occurs:
+Note that rMIDAS uses the [reticulate](https://github.com/rstudio/reticulate) package to interface with Python. Users must have Python 3.5 - 3.8 installed in order to run MIDAS (Python 3.9 is not yet supported). rMIDAS will automatically try to set up a Python configuration unless users specify their own version using `set_python_env()` (examples below). Setting a custom Python install must be performed *before* training or imputing data occurs:
 
 ```{r, eval = FALSE}
 library(rMIDAS)
@@ -79,13 +79,13 @@ set_python_env(python = "conda_env", type = "condaenv", conda = "auto")
 ```
 
 
-## Vignettes
+## Vignettes (including demo)
 
 **rMIDAS** is packaged with two vignettes:
 
-1. `vignette("impute-demo", "rMIDAS")` demonstrates the basic workflow of using the **rMIDAS** package
-2. `vignette("custom-python", "rMIDAS")` provides detailed guidance on configuring Python binaries and environments, including troubleshooting tips
+1. `vignette("impute-demo", "rMIDAS")` demonstrates the basic workflow and capacities of **rMIDAS**
+2. `vignette("custom-python", "rMIDAS")` provides detailed guidance on configuring Python binaries and environments, including some troubleshooting tips
 
 ## Getting help
 
-**rMIDAS** is still in development, and we may not have caught all bugs. If you come across any difficulties, or have any suggestions for improvements, please raise an issue [here](https://github.com/MIDASverse/MIDASpy/issues). 
+rMIDAS is still in development, and we may not have caught all bugs. If you come across any difficulties, or have any suggestions for improvements, please raise an issue [here](https://github.com/MIDASverse/MIDASpy/issues). 

README.md

@@ -20,22 +20,20 @@ accurate and efficient algorithm based on deep learning methods. The
 package provides a simplified workflow for imputing and then analyzing
 data:
 
-
   - `convert()` carries out all necessary preprocessing steps
   - `train()` constructs and trains a MIDAS imputation model
   - `complete()` generates multiple completed datasets from the trained
     model
-  - `combine()` estimates regression models on the complete data,
-    using Rubin’s combination Rules
-
+  - `combine()` runs regression analysis across the complete data,
+    following Rubin’s combination Rules
 
 **rMIDAS** is based on the Python class
 [MIDASpy](https://github.com/MIDASverse/MIDASpy).
 
 ### Efficient handling of large data
 
 rMIDAS also incorporates several features to streamline and improve the
-efficiency of multiple imputation analysis:
+the efficiency of multiple imputation analysis:
 
   - Optimisation for large datasets using `data.table` and `mltools`
     packages