I develop and maintain methods (often collaboratively) for population genetic/genomic analyses on my GitHub page. Here are descriptions of some of these methods, and links to download and use them. Feel free to contact me with bug reports/questions.

IMa2p

Parallelized Isolation with Migration (IM) analyses of haplotypic data to estimate evolutionary demography (effective population sizes, migration rates, and divergence times) of populations. Read our paper on IMa2p, published in Molecular Ecology Resources here. Code is available on my GitHub page here.

IMGui

GUI implementation of IMa2p, allows installation of an 'app' on your PC to enable an easy drag/drop/click interface to specify parameters, run monitoring, and to generate output visualizations. Read our paper on IMGui, published in MBE here. Code and instructions to use IMGui are here.

InRelate

InRelate is a maximum likelihood method for the estimation of pairwise genetic relatedness between two individuals, under the admixture model. Read more about InRelate in my paper published in G3 here. R code and instructions to use InRelate are here.

p-MULTICLUST

Fast and parallelized estimation of genetic ancestry and allele frequencies from polyploid, multilocus, multi-allelic genomic data under the admixture model. Read more about MULTICLUST here (publication on it is coming soon!).

MigSelect

Parallelized framework for the estimation of genomic loci under the effects of selection against migration (genomic islands of speciation), or those under the effects of linked natural selection effects. Read the methods paper here (publication on the parallelized implementation, and applications to genomic data coming soon, but watch this video from Dr. Sethuraman at the SSE meetings to learn more).

IMa3

Parallelized co-estimation of population phylogeny, and evolutionary history under the Isolation with Migration (IM) model. Read more about IMa3 in our publication in MBE here, and download the development build here.

PPP - PopGen Pipeline Platform

An end-end implementation of scripts for QC, filtering, performing summary statistics estimation, and model-based estimation of evolutionary histories from population genomic data. This project is still in development, but you can track our updates here at the PPP development site.