Author: Farimah Houshmand , Farzad Eskandari Ph.D.
Description: Imperialist Competitive Algorithm (ICA) is a computational method that is used to solve optimization problems of different types and it is the mathematical model and the computer simulation of human social evolution.
The package provides a minimum value for the cost function and the best value for the optimization variables by Imperialist Competitive Algorithm.
Users can easily define their own objective function depending on the problem at hand.
This version has been successfully applied to solve optimization problems, for continuous functions.

Title: In-depth Characterization and Analysis of Mutational Signatures
Author: Steve Rozen, Nanhai Jiang, Arnoud Boot, Mo Liu
Description: Analysis and visualization of experimentally elucidated mutational
signatures -- the kind of analysis and visualization in Boot et al.,
"In-depth characterization of the cisplatin mutational signature in
human cell lines and in esophageal and liver tumors", Genome Research 2018,
<doi:10.1101/gr.230219.117>. 'ICAMS' stands for In-depth Characterization
and Analysis of Mutational Signatures. 'ICAMS' has functions to read in
variant call files (VCFs) and to collate the corresponding catalogs of
mutational spectra and to analyze and plot catalogs of mutational spectra
and signatures. Handles both "counts-based" and "density-based" catalogs
Imports: Biostrings, BSgenome, data.table, dplyr, GenomeInfoDb,
GenomicRanges, graphics, grDevices, IRanges, RColorBrewer,

"Characterization of colibactin-associated mutational signature in an
Asian oral squamous cell carcinoma and in other mucosal tumor types",
'ICAMS' stands for In-depth Characterization

Title: In-Depth Characterization and Analysis of Mutational Signatures
Author: Steve Rozen, Nanhai Jiang, Arnoud Boot, Mo Liu, Yang Wu

Title: Imperialist Competitive Algorithm for Optimal Designs
Authors@R: c(person("Ehsan", "Masoudi", role = c("aut", "cre"),  email = "ehsan.masoudi@wwu.de"),  person("Heinz", "Holling", role = "aut"),  person("Weng Kee", "Wong", role = "aut"))
Description: Finding locally D-optimal, minimax D-optimal, standardized maximin D-optimal, optim-on-the-average and multiple objective optimal designs for nonlinear models. Different Fisher information matrices can also be set by user. There are also  useful functions for verifying the optimality of the designs with respect to different criteria by equivalence theorem. ICA is a meta-heuristic evolutionary algorithm inspired from the socio-political process of humans. See Masoudi et al. (2016) <doi:10.1016/j.csda.2016.06.014>.
Imports: Rcpp, Rsolnp, nloptr, stats, graphics, grDevices