taskviewr is a Shiny application for browsing R packages listed on CRAN's Task Views. It includes their URLs and licensing details, which can be very helpful if you are looking for, say, a machine learning package that is MIT-licensed. The source code is available on GitHub.
A package for MultiLabel Prediction Using Gibbs Sampling. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications. More details are available here. Source & development version installation instructions on GitHub. Now available on CRAN.
A wrapper for the CanIStream.It API for searching across the most popular streaming, rental, and purchase services to find where a movie is available. Source & development version installation instructions on GitHub Now available on CRAN.
A wrapper for the Wikidata Query Service API for querying Wikidata using SPARQL and getting back data.frames in R. Source & development version installation instructions on GitHub Now available on CRAN.
A little utility R package for transforming time series data into a format that's more machine learning-friendly -- previous p observations become features. Source & development version installation instructions on GitHub.
An R package for Bayesian Categorical Data Aanalysis of 2×2 contingency tables. I developed this package for internal use at Wikimedia Foundation for the analysis of A/B tests. Source & development version installation instructions on GitHub. Coming eventually to CRAN.
An R package for Less Restrictive Tree-based Classification and Regression. Motivated by the restricted GPL licensing of almost every machine learning package available on CRAN, this package implements CART and random forests under the less restrictive MIT license. Source & installation instructions on GitHub