Merge pull request #54 from rsquaredacademy/develop

Release candidate

Author: aravindhebbali

.Rbuildignore

@@ -10,4 +10,5 @@
 ^appveyor\.yml$
 ^codecov\.yml$
 ^docs$
-^hex_blorr\.png$
+^hex_blorr\.png$
+^cran-comments\.md$

DESCRIPTION

@@ -3,7 +3,7 @@ Type: Package
 Title: Tools for Developing Binary Logistic Regression Models
 Version: 0.1.0
 Authors@R: person("Aravind", "Hebbali", email = "hebbali.aravind@gmail.com", role = c("aut", "cre"))
-Description: Tools designed to make it easier for beginner/intermediate users to build and validate 
+Description: Tools designed to make it easier for beginner and intermediate users to build and validate 
     binary logistic regression models. Includes bivariate analysis, comprehensive regression output, 
     model fit statistics, variable selection procedures, model validation techniques and a 'shiny' 
     app for interactive model building.

NEWS.md

@@ -1,6 +1,3 @@
 # blorr 0.1.0
 
-Initial release
-
-
-
+Initial release

README.Rmd

@@ -26,9 +26,11 @@ output, variable selection procedures, model validation techniques and a 'shiny'
 
 ### Installation
 
-You can install blorr from github with:
-
 ```{r gh-installation, eval = FALSE}
+# Install blorr from CRAN
+install.packages("blorr")
+
+# Or the development version from GitHub
 # install.packages("devtools")
 devtools::install_github("rsquaredacademy/blorr")
 ```
@@ -39,12 +41,7 @@ Use `blr_launch_app()` to explore the package using a shiny app.
 
 ## Vignettes
 
-- [Bivariate Analysis](https://blorr.rsquaredacademy.com/bivariate_analysis.html)
-- [Model Fit Statistics](https://blorr.rsquaredacademy.com/intro.html)
-- [Model Validation](https://blorr.rsquaredacademy.com/model_validation.html)
-- [Variable Selection](https://blorr.rsquaredacademy.com/variable_selection.html)
-- [Residual Diagnostics](https://blorr.rsquaredacademy.com/residual_diagnostics.html)
-
+- [A Short Introduction to the blorr Package](https://blorr.rsquaredacademy.com/articles/introduction.html)
 
 ### Consistent Prefix
 

README.md

@@ -11,9 +11,11 @@ Tools designed to make it easier for users, particularly beginner/intermediate R
 
 ### Installation
 
-You can install blorr from github with:
-
 ``` r
+# Install blorr from CRAN
+install.packages("blorr")
+
+# Or the development version from GitHub
 # install.packages("devtools")
 devtools::install_github("rsquaredacademy/blorr")
 ```
@@ -25,11 +27,7 @@ Use `blr_launch_app()` to explore the package using a shiny app.
 Vignettes
 ---------
 
--   [Bivariate Analysis](https://blorr.rsquaredacademy.com/bivariate_analysis.html)
--   [Model Fit Statistics](https://blorr.rsquaredacademy.com/intro.html)
--   [Model Validation](https://blorr.rsquaredacademy.com/model_validation.html)
--   [Variable Selection](https://blorr.rsquaredacademy.com/variable_selection.html)
--   [Residual Diagnostics](https://blorr.rsquaredacademy.com/residual_diagnostics.html)
+-   [A Short Introduction to the blorr Package](https://blorr.rsquaredacademy.com/articles/introduction.html)
 
 ### Consistent Prefix
 

cran-comments.md

@@ -0,0 +1,10 @@
+## Test environments
+* local Windows 10, R 3.5.0
+* ubuntu 14.04 (on travis-ci), R 3.4.4, R 3.5.0, R-devel
+* win-builder (devel and release)
+
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* This is a new release.

docs/LICENSE.html

@@ -15,14 +15,14 @@ knitr::opts_chunk$set(
 )
 ```
 
-### Introduction
+## Introduction
 
 The blorr package offers tools for building and validating binary logistic regression models. It 
 is most suitable for beginner/intermediate R users and those who teach statistics using R. The API
 is very simple and most of the functions take either a `data.frame`/`tibble` or a `model` as input. **blorr** use
 consistent prefix **blr_** for easy tab completion.
 
-## Installation
+### Installation
 
 You can install **blorr** using:
 
@@ -42,7 +42,7 @@ library(blorr)
 library(magrittr)
 ```
 
-### Data
+## Data
 
 To demonstrate the features of blorr, we will use the bank marketing data set. 
 The data is related with direct marketing campaigns of a Portuguese banking 
@@ -52,7 +52,7 @@ product (bank term deposit) would be ('yes') or not ('no') subscribed. It
 contains a random sample (~4k) of the original data set which can be found 
 at https://archive.ics.uci.edu/ml/datasets/bank+marketing.
 
-### Bivariate Analysis
+## Bivariate Analysis
 
 Let us begin with careful bivariate analysis of each possible variable and the 
 outcome variable. We will use information value and likelihood ratio chi square 
@@ -102,7 +102,7 @@ blr_woe_iv_stats(bank_marketing, y, job, marital, education)
 `blr_woe_iv()` and `blr_woe_iv_stats()` are currently avialable for categorical 
 predictors only.
 
-### Stepwise Selection
+## Stepwise Selection
 
 For the initial/ first cut model, all the independent variables are put into 
 the model. Our goal is to include a limited number of independent variables 
@@ -133,6 +133,8 @@ model %>%
   plot()
 ```
 
+## Regression Output
+
 ### Model
 
 We can use bivariate analysis and stepwise selection procedure to shortlist 
@@ -146,8 +148,6 @@ model <- glm(y ~  age + duration + previous + housing + default +
              family = binomial(link = 'logit'))
 ```
 
-### Regression Output
-
 Use `blr_regress()` to generate comprehensive regression output. It accepts 
 either of the following
 
@@ -175,7 +175,7 @@ blr_regress(y ~  age + duration + previous + housing + default +
              loan + poutcome + job + marital, data = bank_marketing)
 ```
 
-### Model Fit Statistics
+## Model Fit Statistics
 
 Model fit statistics are available to assess how well the model fits the data 
 and to compare two different models.The output includes likelihood ratio test,