Suggests: testthat (>= 2.1.0), knitr, aniDom, rmarkdown
Packaged: 2020-03-12 14:24:54 UTC; christofneumann

Title: Bayesian Dominance Hierarchy Steepness via Elo Rating and
Description: Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2022) <doi:10.1101/2022.01.28.478016>). Steepness estimation is based on Bayesian implementations of either Elo-rating or David's scores.
Suggests: rmarkdown, bookdown, xtable, knitr, testthat (>= 3.0.0)
Packaged: 2022-08-16 12:40:59 UTC; christofneumann
Author: Christof Neumann [aut, cre] (<https://orcid.org/0000-0002-0236-1219>)

Packaged: 2022-10-11 21:01:09 UTC; christofneumann

Description: Flexible implementation of Elston-Stewart algorithm

Description: Flexible implementation of Elston-Stewart algorithm.

Title: Fast Embedding Guided by Self-Organizing Map
Suggests: ggplot2, Matrix, igraph, Rtsne, umap, uwot, knitr, rmarkdown
Description: Provides a smooth mapping of multidimensional points into
low-dimensional space defined by a self-organizing map. Designed to work
with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019)

Imports: FNN, ggplot2, igraph, Matrix, Rtsne, umap, uwot

Title: Computation and Visualization of Empirical Joint Distribution
Author: En-shuo Hsu, Jeffrey C. Miecznikowski
Description: Computes and visualizes empirical joint distribution of multivariate data with optimized algorithms and multi-thread computation. There is a faster algorithm using dynamic programming to compute the whole empirical joint distribution of a bivariate data. There are optimized algorithms for computing empirical joint CDF function values for other multivariate data. Visualization is focused on bivariate data. Levelplots and wireframes are included.

person("Davide", "Pagano", email = "davide.pagano@unibs.it", role = c("aut","cre")),
person("Lorenzo", "Sostero", email = "l.sostero@studenti.unibs.it", role = "aut"))
Description: A C++ implementation of the following evolutionary
Genetic Algorithms (Holland, 1992, ISBN:978-0262581110),
Gravitational Search Algorithm (Rashedi et al., 2009 <doi:10.1016/j.ins.2009.03.004>),
Grey Wolf Optimization (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>),
Improved Harmony Search (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>),
Moth-flame Optimization (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>),
Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959),
Simulated Annealing (Kirkpatrick et al., 1983 <doi:10.1126/science.220.4598.671>),
Whale Optimization Algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>).
'EmiR' can be used not only for unconstrained optimization problems, but also
in presence of inequality constrains, and variables restricted to be integers.
Imports: Rcpp (>= 1.0.5), methods, Rdpack, tictoc, ggplot2, tibble,
tidyr, dplyr, gganimate, mathjaxr, data.table, plot3D
Packaged: 2021-08-02 08:00:48 UTC; davidepagano