Computing F-Statistics from Pool-Seq Data (poolfstat)
Functions for the computation of F-statistics from Pool-Seq data in population genomics studies. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (‘vcf’ and ‘rsync’ files as obtained from the popular software ‘VarScan’ and ‘PoPoolation’ respectively) and perform conversion to alternative format (as used in the ‘BayPass’ and ‘SelEstim’ software).

Voting Systems, Instant-Runoff Voting, Borda Method, Various Condorcet Methods (votesys)
Various methods to count ballots in voting systems are provided: Instant-runoff voting described in Reynolds, Reilly and Ellis (2005, ISBN:9789185391189), Borda method in Emerson (2013) <doi:10.1007/s00355-011-0603-9>, original Condorcet method in Stahl and Johnson (2017, ISBN:9780486807386), Dodgson method in McCabe-Dansted and Slinko (2008) <doi:10.1007/s00355-007-0282-8>, Simpson-Kramer method in Levin and Nalebuff (1995) <doi:10.1257/jep.9.1.3>, Schulze method in Schulze (2011) <doi:10.1007/s00355-010-0475-4>, Ranked pairs method in Tideman (1987) <doi:10.1007/BF00433944>. Functions to check validity of ballots are also provided to ensure flexibility.

A Graph Based Particle Simulator Based on D3-Force (particles)
Simulating particle movement in 2D space has many application. The ‘particles’ package implements a particle simulator based on the ideas behind the ‘d3-force’ ‘JavaScript’ library. ‘particles’ implements all forces defined in ‘d3-force’ as well as others such as vector fields, traps, and attractors.

3D Forest Simulation Visualization Tool (DGVM3D)
This is a visualization tool for vegetation structure/succession in space and/or time mainly for forest gap models. However, it could also be used to visualize observed forest stands. If used for models, they should contain either individual trees or cohorts (e.g. LPJ-GUESS by Smith et al. (2014) <doi:10.5194/bg-11-2027-2014>). For a list of required and additional data fields see the vignette.

Dependence Measures via Energy Statistics (EDMeasure)
Implementations of (1) mutual dependence measures and mutual independence tests in Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.0253>; (2) independent component analysis methods based on mutual dependence measures in Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.0253> and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>; (3) conditional mean dependence measures and conditional mean independence tests in Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012> and Park, T., et al. (2015) <doi:10.1214/15-EJS1047>.