# Software (R packages)

The following are R packages that I have (co)authored and/or that I maintain. Some of them have their most recent versions on github. Most packages are also on CRAN.

## Time series forecasting

**Rlgt**implements Local and Global Trend models, which is a novel class of exponential smoothing models, and fits them with Bayesian model fitting [CRAN]**forecast**is the most popular package for forecasting in R. It is developed by Rob J. Hyndman. I re-implemented part of the ets() function in C++, achieving considerable speedups [CRAN]**tsDyn**implements threshold autoregressive models. I implemented some patches and some (currently unpublished) extensions, mainly regarding neuro-coefficient STAR models [CRAN]**tsExpKit**is a framework for time series experiments, aimed at the facilitation of performing well-documented, reproducible experimentation [github]**Rsiopred**is an R package for forecasting by exponential smoothing with model selection by a fuzzy multicriteria approach (currently unpublished).**Mcomp**contains data of the M3 forecasting competition. Its author is Rob J. Hyndman. I included the forecasts of the original competition participants into the package [CRAN]

## General-purpose machine learning

**RSNNS**is a comprehensive package for neural networks in R [CRAN]**Rmalschains**implements memetic algorithms in R [CRAN]**frbs**is a comprehensive package for fuzzy rule-based systems in R. It is mainly developed by Lala S. Riza, and I maintain it. [CRAN]**RoughSets**is a comprehensive package for rough set and fuzzy rough set theory in R. It is mainly developed by my collaborators, and I maintain it [CRAN]**ssc**is a package for semi-supervised classification in R. It is mainly developed by Mabel Gonzalez, and I maintain it [CRAN]**opusminer**provides an interface to the OPUS Miner algorithm (implemented in C++) for finding the key associations in transaction data efficiently, in the form of self-sufficient itemsets, using either leverage or lift. It is published on CRAN, I maintain it [CRAN]**ChoR**is a wrapper for the java-implemented Chordalysis algorithm, which learns the structure of graphical models from datasets with thousands of variables [CRAN]