Regularized Principal Component Analysis for Spatial Data (SpatPCA)
This package provides regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions by using alternating direction method of multipliers (ADMM) algorithm.

Regularized Multivariate Regression for Identifying Master Predictors (remMap)
remMap is developed for fitting multivariate response regression models under the high-dimension-low-sample-size setting

Interactive Visualization of Topic Models (LDAvis)
Tools to create an interactive web-based visualization of a topic model that has been fit to a corpus of text data using Latent Dirichlet Allocation (LDA). Given the estimated parameters of the topic model, it computes various summary statistics as input to an interactive visualization built with D3.js that is accessed via a browser. The goal is to help users interpret the topics in their LDA topic model.

Nearest-neighbor Classification with Categorical Variables (knncat)
Scale categorical variables in such a way as to make NN classification as accurate as possible. The code also handles continuous variables and prior probabilities, and does intelligent variable selection and estimation of both error rates and the right number of NN’s.

Assertive programming for R analysis pipelines (assertr)
The assertr package supplies a suite of functions designed to verify assumptions about data early in an dplyr/magrittr analysis pipeline so that data errors are spotted early and can be addressed quickly.

Rcpp Bindings for Annoy, a Library for Approximate Nearest Neighbors (RcppAnnoy)
Annoy is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the Rcpp and BH packages, exposing the same interface as the original Python wrapper to Annoy. See for more on Annoy. Annoy is released under Version 2.0 of the Apache License. Also included is a small Windows port of mmap which is released under the MIT license.