
grpSLOPE
— Group SLOPE is a penalized linear regression method that is used for adaptive selection of groups of significant predictors in a highdimensional linear model. A unique feature of the Group SLOPE method is that it offers (group) false discovery rate control (i.e., control of the expected proportion of irrelevant groups among the total number of groups of predictors selected by the Group SLOPE method). This R package is associated with Brzyski et. al.(2018) “Group SLOPE  adaptive selection of groups of predictor” (arXiv version).
github ghpages CRAN 
grpSLOPEMC
— This is an extension package to the R package grpSLOPE. It contains Monte Carlo based methods for the estimation of the regularizing sequence. This R package is associated with Gossmann et. al. (2017) “A sparse regression method for groupwise feature selection with false discovery rate control”.
github 
FDRcorrectedSCCA
— Codes associated with the publication Gossmann et. al. (2018) “FDRCorrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics” (arXiv version) with all methods and algorithms organized as functions in an R package for convenience.
github 
mixed_models
— Fit statistical (linear) models with fixed and mixed (random) effects in Ruby. The package supports the formula language of the R packagelme4
for model specification (a pain to implement), many types of hypotheses tests and confidence intervals for the fixed and random effects coefficients, methods for prediction and prediction intervals, etc. It is my Google Summer Of Code 2015 project.
github rubygems

spitzy
— Spitzy is the name of a cute Pomeranian. Spitzy reads backwards as yztips, which translates into: Your ZappyTappy Initial and boundary value Partial (and ordinary) differential equation Solver. It is my collection of numerical methods for differential equations written in Ruby.
github ghpages rubygems 
When I have some free time, I like to contribute code to open source software projects (mostly, related to mathematics, statistics or machine learning). Visit my github page for the projects that I contribute to.