MOBSTER (Caravagna et al. 2020)
Subclonal reconstruction in cancer by combining evolutionary theory with machine learning:
https://caravagn.github.io/mobster

MCMC-MutationalDistances (Werner et al. 2020)
Inference of de-coupled single cell microscopic parameters such as the mutation rates per division and the cell death rates from multi-sampling genomic data:
https://github.com/sottorivalab/MCMC-MutationalDistances-

CHESS (Chkhaidze et al. 2019)
A spatial model of tumour growth that also simulates different sampling strategies and the generation of genomic data:
https://github.com/sottorivalab/CHESS.cpp

REVOLVER (Caravagna et al. 2018)
Detecting repeated evolutionary trajectories in cancer using Transfer Learning:
https://github.com/sottorivalab/revolver

Quantifying-Selection (Williams et al. 2016; Williams et al. 2018)
A set of tools to measure neutral evolution and subclonal selection using either a frequentist approach or an Approximate Bayesian Computation approach for model selection:
https://marcjwilliams1.github.io/quantifying-selection