Merge pull request #6 from UBC-MDS/tariq-dev

Tariq dev

R/hello.R

@@ -0,0 +1,18 @@
+# Hello, world!
+#
+# This is an example function named 'hello' 
+# which prints 'Hello, world!'.
+#
+# You can learn more about package authoring with RStudio at:
+#
+#   http://r-pkgs.had.co.nz/
+#
+# Some useful keyboard shortcuts for package authoring:
+#
+#   Build and Reload Package:  'Cmd + Shift + B'
+#   Check Package:             'Cmd + Shift + E'
+#   Test Package:              'Cmd + Shift + T'
+
+hello <- function() {
+    print("Hello, world!")
+}

README.md

@@ -1,7 +1,14 @@
 # PunisheR
 
-The PunisheR package will implement techniques for feature and model selection. Namely, it will contain tools for forward and backward selection, as well as tools for computing AIC and BIC (see below). 
+PunisheR is a package for feature and model selection in R. Specifically, this package will implement tools for
+forward and backward model selection (see [here](https://en.wikipedia.org/wiki/Stepwise_regression)).
+In order to measure model quality during the selection procedures, we will also be implement
+the Akaike and Bayesian Information Criterion (see below), both of which *punish* complex models -- hence this package's
+name.
 
+As examined below, we recognize that well-designed versions of these tools already exist in R.
+This is acceptable to us because impetus for this project is primarily pedagogical, intended to
+improve our understanding of model selection techniques and collaborative software development.
 
 ## Contributors: 
 
@@ -22,7 +29,11 @@ We will also be implementing metrics that evaluate model performance:
 
 ## How the packages fit into the existing R and Python ecosystems ?
 
-In Python ecosystem, forward selection has been implemented in scikit learn by the 
-[f_regression](http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html) function. The function uses Linear model for testing the individual effect of each of many regressors. It has been implemented as a scoring function to be used in feature seletion procedure. The backward selection has also been implemented in scikit learn by the [RFE](http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html) function. RFE uses an external estimator that assigns weights to features and it prunes the number of features by recursively considering smaller and smaller sets of features until the desired number of features to select is eventually reached. Whereas, in R ecosystem, forward and backward selection are implemented by [olsrr package](https://cran.r-project.org/web/packages/olsrr/)
-and in [MASS package](https://cran.r-project.org/web/packages/MASS/MASS.pdf) by function 
-[StepAIC](https://stat.ethz.ch/R-manual/R-devel/library/MASS/html/stepAIC.html). StepAIC performs stepwise selection (forward, backward, both) by exact AIC.
+In the R ecosystem, forward and backward selection are implemented in both the [olsrr](https://cran.r-project.org/web/packages/olsrr/)
+and [MASS](https://cran.r-project.org/web/packages/MASS/MASS.pdf) packages. The former provides
+[`ols_step_forward()`](https://www.rdocumentation.org/packages/olsrr/versions/0.4.0/topics/ols_step_forward) and
+[`ols_step_backward()`](https://www.rdocumentation.org/packages/olsrr/versions/0.4.0/topics/ols_step_backward) for
+forward and backward stepwise selection, respectively. Both of these are p-value-based methods of feature selection.
+The latter, MASS, contains [`StepAIC()`](https://stat.ethz.ch/R-manual/R-devel/library/MASS/html/stepAIC.html),
+which is complete with three modes: forward, backward or both. The selection procedure it uses is based on an
+information criterion (AIC), as we intend ours to be.