Simulates somatic single base substitutions carried in cancer genomes. By only providing a human reference genome, substitutions that result from mutational processes operative in every cancer genome can be generated.

Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.

Algorithms to create prognostic biomarkers using biological genesets or networks.

Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.

A toolkit for simulation studies concerning time-to-event endpoints
    with non-proportional hazards. 'SimNPH' encompasses functions for simulating
    time-to-event data in various scenarios, simulating different trial designs
    like fixed-followup, event-driven, and group sequential designs. The package
    provides functions to calculate the true values of common summary statistics
    for the implemented scenarios and offers common analysis methods for
    time-to-event data. Helper functions for running simulations with the
    'SimDesign' package and for aggregating and presenting the results are also
    included. Results of the conducted simulation study are available as
    preprint: "A neutral comparison of statistical methods for time-to-event
    analyses under non-proportional hazards", Klinglmueller et al. (2023)
    <doi:10.48550/ARXIV.2310.05622>.