A lot of the software I have developed is on GitHub. In particular one may find there respositories connected to some of my more recent publications.

R packages on CRAN

1. GLSME: Generalized Least Squares with Measurement Error
   Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
2. mvSLOUCH: Multivariate Stochastic Linear Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses
   Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) and K. Bartoszek, J. Tredgett Clarke, J. Fuentes-Gonzalez, V. Mitov, J. Pienaar, M. Piwczynski, R. Puchalka, K. Spalik, K. L. Voje (2024) .
3. pcmabc: Approximate Bayesian Computations for Phylogenetic Comparative Methods
   Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2019).
4. PCMBase: Simulation and Likelihood Calculation of Phylogenetic Comparative Models
   Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site provides access to the documentation and other resources.
5. RMaCzek : Czekanowski's Diagrams
   Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020), K. Bartoszek, Y. Luo (2023) and K. Bartoszek, Y. Luo (2024).

Numerical solving Partial Differential Equations

1. FVM: C++ source code for a multidimensional Finite Volume Method for Poisson's equation. See M. Asadzadeh, K. Bartoszek (2014).

R packages developed by my PhD students in connection to supervised projects

1. glinvci: Phylogenetic Comparative Methods with Uncertainty Estimates
   Developed by Woodrow Hao Chi Kiang. A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019). The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models. See Kiang (2024).
2. bgphy: R package for Bayesian Inference of Mixed Gaussian Phylogenetic Models.
   Developed by Bayu Brahmantio. See B. Brahmantio, K. Bartoszek, E. Yapar (2024).