documentation updates for CRAN resubmission

DESCRIPTION

@@ -1,11 +1,12 @@
 Package: fairness
 Title: Algorithmic Fairness Metrics
-Version: 1.1.1
+Version: 1.1.0
 Authors@R: c(person('Nikita', 'Kozodoi', email = '[email protected]', role = c('aut', 'cre')),
             person('Tibor', 'V. Varga', email = '[email protected]', role = c('aut'), comment = c(ORCID = '0000-0002-2383-699X')))
 Maintainer: Nikita Kozodoi <[email protected]>
 Description: Offers various metrics of algorithmic fairness. Fairness in machine learning is an emerging topic with the overarching aim to critically assess algorithms (predictive and classification models) whether their results reinforce existing social biases. While unfair algorithms can propagate such biases and offer prediction or classification results with a disparate impact on various sensitive subgroups of populations (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities), fair algorithms possess the underlying foundation that these groups should be treated similarly / should have similar outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) <doi:10.1007/s10618-010-0190-x>, Chouldechova (2017) <doi:10.1089/big.2016.0047>, Feldman et al. (2015) <doi:10.1145/2783258.2783311> , Friedler et al. (2018) <doi:10.1145/3287560.3287589> and Zafar et al. (2017) <doi:10.1145/3038912.3052660>. The package also offers convenient visualizations to help understand fairness metrics.
 License: MIT + file LICENSE
+Language: en-US
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.1.0

NEWS.md

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-# fairness 1.1.1
-- fixed `fnr_parity()` and `fpr_parity()` calculations for different outcome bases
-
 # fairness 1.1.0
 - fixed `outcome_levels` issue when levels of provided predictions do not match outcome levels
-- renamed `outcome_levels` to `preds_levels` to imporve clarity
+- renamed `outcome_levels` to `preds_levels` to improve clarity
 - added `outcome_base` argument to set base level for target variable used to compute fairness metrics
+- fixed `fnr_parity()` and `fpr_parity()` calculations for different outcome bases
 - updates in package documentation
 
 # fairness 1.0.2

README.md

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 [![minimal R version](https://img.shields.io/badge/R%3E%3D-3.6.0-6666ff.svg)](https://cran.r-project.org/)
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/fairness)](https://www.r-pkg.org/badges/version/fairness)
-[![packageversion](https://img.shields.io/badge/Package%20version-1.1.1-orange.svg?style=flat-square)](commits/master)
+[![packageversion](https://img.shields.io/badge/Package%20version-1.1.0-orange.svg?style=flat-square)](commits/master)
 
 ---
 

cran-comments.md

@@ -0,0 +1,23 @@
+## Resubmission
+This is a resubmission. In this version I have:
+* Updated version number to 1.1.0
+* Fixed `outcome_levels` issue when levels of provided predictions do not match outcome levels
+* Renamed `outcome_levels` to `preds_levels` to imporve clarity
+* Added `outcome_base` argument to metric functions to set a base level for target variable used to compute fairness metrics
+* Fixed `fnr_parity()` and `fpr_parity()` calculations for different outcome bases
+* Updated the documentation icluding README, LICENSE and vignettes
+* Updated the contact e-mail
+
+
+## Test environments
+* local OS X install, R 3.6.0
+* ubuntu 14.04 (on travis-ci), R 3.5.0
+* win-builder (R-devel, R-release)
+
+
+## R CMD check results
+There were no ERRORs, WARNINGs or NOTEs.
+
+
+## Downstream dependencies
+There are currently no downstream dependencies for this package.