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 ðŸ™‚), 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, Iâ€™m working or have worked on.

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:* 2022-11-14 20:49:33

`fcci`

*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].

`kgrams`

*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.

`scribblr`

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

`runi`

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

`r2r`

*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.

`gsample`

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

`sbo`

*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, https://www.aclweb.org/anthology/D07-1090/).

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

*Optimal paths in Hidden Markov Models.* C++ implementation of Viterbiâ€™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 Dijkstraâ€™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 https://github.com/vgherard/vgherard.github.io/, 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 ...".