Implements Dirichlet multinomial modeling of relative abundance data using functionality provided by the 'Stan' software. The purpose of this package is to provide a user friendly way to interface with 'Stan' that is suitable for those new to modeling. For more regarding the modeling mathematics and computational techniques we use see our publication in Molecular Ecology Resources titled 'Dirichlet multinomial modeling outperforms alternatives for analysis of ecological count data' (Harrison et al. 2020 <doi:10.1111/1755-0998.13128>).

Provides the ability to create interaction maps, discover CNV map domains (edges), gene annotate interactions, and create interactive visualizations of these CNV interaction maps.

Coalescent simulators can rapidly simulate biological sequences
    evolving according to a given model of evolution.
    You can use this package to specify such models, to conduct the simulations
    and to calculate additional statistics from the results (Staab, Metzler,
    2016 <doi:10.1093/bioinformatics/btw098>).
    It relies on existing simulators for doing the simulation, and currently
    supports the programs 'ms', 'msms' and 'scrm'. It also supports finite-sites
    mutation models by combining the simulators with the program 'seq-gen'.
    Coala provides functions for calculating certain summary statistics, which
    can also be applied to actual biological data.
    One possibility to import data is through the 'PopGenome' package
    (<https://github.com/pievos101/PopGenome>).

Flexible framework for coalescent analyses in R. It includes a main function running the  MCMC algorithm, auxiliary functions for tree rearrangement, and some functions to compute population genetic parameters. Extended description can be found in Paradis (2020) <doi:10.1201/9780429466700>. For details on the MCMC algorithm, see Kuhner et al. (1995) <doi:10.1093/genetics/140.4.1421> and Drummond et al. (2002) <doi:10.1093/genetics/161.3.1307>.

A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data.
    More details can be referred to Liu et al. (2024) <doi:10.1093/biomtc/ujae031>.