In my day to day work, I routinely program with Python, SQL, R and Bash. For my personal/fun projects (which is what this page is about 🙂), my preference goes for R and C++, and my main topics of interest are statistical computing, machine-learning and natural language processing.

This page contains a selected list of open-source projects, mostly but not only R packages, I’m working or have worked on.

R packages

The R packages listed below can be installed from my R-universe public repository. This list is automatically updated every day, round midnight (UTC).

Last update: 2022-07-04 01:42:15


CRAN status

Feldman-Cousins Confidence Intervals. Provides support for building Feldman-Cousins confidence intervals [G. J. Feldman and R. D. Cousins (1998) doi:10.1103/PhysRevD.57.3873].


CRAN status

Classical k-gram Language Models. Tools for training and evaluating k-gram language models in R, supporting several probability smoothing techniques, perplexity computations, random text generation and more.


CRAN status

A Notepad Inside RStudio. A project aware notepad inside RStudio, for taking quick project-related notes without distractions. RStudio addin.


CRAN status

Client for R-universe APIs. Client for R-universe APIs.


CRAN status

R-Object to R-Object Hash Maps. Implementation of hash tables (hash sets and hash maps) in R, featuring arbitrary R objects as keys, arbitrary hash and key-comparison functions, and customizable behaviour upon queries of missing keys.


CRAN status

Efficient Weighted Sampling Without Replacement. Sample without replacement using the Gumbel-Max trick (c.f. ).


CRAN status

Text Prediction via Stupid Back-Off N-Gram Models. Utilities for training and evaluating text predictors based on Stupid Back-Off N-gram models (Brants et al., 2007,

Code excerpts

This Section lists some smaller projects, including code snippets, algorithm implementations and similars.


Optimal paths in Hidden Markov Models. C++ implementation of Viterbi’s dynamic programming algorithm for finding optimal paths in the hidden space of hidden Markov models.


Minimum length paths in directed graphs. C++ implementation of Dijkstra’s algorithm for finding minimum length paths in weighted directed graphs.


If you see mistakes or want to suggest changes, please create an issue on the source repository.


Text and figures are licensed under Creative Commons Attribution CC BY-SA 4.0. Source code is available at, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".