Software

I am the lead developer and maintainer of several widely used extension packages for the R software environment for statistical computing and graphics. R and Python are the two most widely used platforms for Data Science, ML, and AI.

My R packages are also available on R-universe.

Development versions of our software are available on GitHub.

Python packages published via the Python Package Index.

Software Sections

Association Rule Mining
Bioinformatics
Combinatorial Optimization
Data Stream Mining
Recommender Systems
Automated Planning and Optimal Control
Educational Software
Continuous integration Tests

Association Rule Mining

arules: Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. Hahsler, Gruen and Hornik (2005) <doi:10.18637/jss.v014.i15>.
arulesViz: Extends package arules with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration. Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.
arulesSequences: Add-on package to handle and mine frequent sequences (lead developer: Christian Buchta).
arulesCBA: Provides the infrastructure for association rule-based classification including the algorithms CBA, CMAR, CPAR, C4.5 FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers. <doi: 10.32614/RJ-2019-048>
arulesNBMiner: NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi: 10.1007/s10618-005-0026-2> (preprint).

Bioinformatics

rRDP: Seamlessly interfaces the Ribosomal Database Project (RDP) classifier (version 2.9) which implements a Naive Bayesian Classifier (NBC) for biological sequences. [intro]
rMSA: Interface for Popular Multiple Sequence Alignment Tools like ClustalW, MAFFT, MUSCLE and Kalign. [intro]
rBLAST: Interfaces the Basic Local Alignment Search Tool (BLAST) to search genetic sequence databases with the Bioconductor infrastructure.
QuasiAlign: Efficient sequence alignment using alignment-free methods. [Prototype at R-Forge]

Combinatorial Optimization

dbscan: A fast reimplementation of several density-based algorithms of the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial clustering of applications with noise) and OPTICS (ordering points to identify the clustering structure) clustering algorithms HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier factor) algorithm. The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. See Hahsler M, Piekenbrock M and Doran D (2019) <doi:10.18637/jss.v091.i01>.
seriation: Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler, Hornik and Buchta (2008) <doi:10.18637/jss.v025.i03>.
TSP: Basic infrastructure and some algorithms for the traveling salesperson problem (also traveling salesman problem; TSP). The package provides some simple algorithms and an interface to the Concorde TSP solver and its implementation of the Chained-Lin-Kernighan heuristic. The code for Concorde itself is not included in the package and has to be obtained separately. Hahsler and Hornik (2007) <doi:10.18637/jss.v023.i02>.
qap: Implements heuristics for the Quadratic Assignment Problem (QAP). Currently, only a simulated annealing heuristic by Burkard and Rendl (1984) <doi: 10.1016/0377-2217(84)90231-5> is available .

Data Stream Mining

stream: Infrastructure for data stream mining (supported in part by NSF IIS-0948893 and NIH R21HG005912). Hahsler, Bolaños and Forrest (2017) <doi: 10.18637/jss.v076.i14>.
streamMOA: Interface to MOA’s data stream clustering algorithms. [ intro]
rEMM: Implementation of the Extensible Markov Model (EMM) which adds a temporal component to data stream clustering by superimposing a dynamically adapting Markov Chain. This research is supported by NSF Grant 0948893. Hahsler and Dunham (2010) <doi: 10.18637/jss.v035.i05>.

Recommender Systems

recommenderlab: Provides the infrastructure to test and develop recommender algorithms. Currently contains implementations for UBCF, IBCF, FunkSVD, popular items and association rules and supports a wide range of evaluation techniques. [ intro, online example]

Automated Planning and Optimal Control

pomdp: Provides the infrastructure to define, solve, and analyze the solutions of Partially Observable Markov Decision Processes (POMDP) models.
pomdpSolve: The package provides the code for pomdp-solve to solve POMDPs using a variety of exact and approximate value iteration algorithms developed by Anthony R. Cassandra.

Educational Software

Data Mining: R code examples for the textbook Introduction to Data Mining (Tan/Steinbach/Kumar).
Artifical Intelligence: Slides, simple Python code examples and exercises for an introduction course to AI using the textbook Artificial Intelligence: A Modern Approach (Russell/Norvig).
fit_dist: A simple R script to fit distributions to data.
Gridhunt2: A simple game used to teach students basic OO concepts (encapsulation, composition, inheritance, and polymorphism) using C++.