Title: Application of MOSS algorithm to genome-wide association study
Author: Olga Vesselova, Matthew Friedlander, Laurent Briollais, Adrian Dobra, Helene Massam.
Description: This is a genMOSS package with additional datafile preprocessing functions. Performs genome-wide analysis of dense SNP array data using the mode oriented stochastic search (MOSS) algorithm in a case-control design. The MOSS algorithm is a Bayesian variable selection procedure that is applicable to GWAS data. It identifies combinations of the best predictive SNPs associated with the response. It also performs a hierarchical log-linear model search to identify the most relevant associations among the resulting subsets of SNPs. This package also includes preprocessing of the data from Plink format to the format required by the MOSS algorithm.

Title: Predict Epidemic Curves with Generalized SEIR Modeling
Imports: pracma, minpack.lm, nlsr, ggplot2
Description: Performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) <doi:10.1101/2020.02.16.20023465>.
Packaged: 2021-06-21 19:51:07 UTC; selcukkorkmaz

Packaged: 2021-07-12 13:59:56 UTC; selcukkorkmaz
Author: Selcuk Korkmaz [aut, cre] (<https://orcid.org/0000-0003-4632-6850>)

Title: Generating multi-state survival data
Author: Artur Agostinho Araújo, Luís Meira-Machado
Maintainer: Artur Agostinho Araújo <b5498@math.uminho.pt>
Description: Generation of survival data with one (binary)
time-dependent covariate.  Generation of survival data arising

Author: Artur Agostinho Araujo, Luis Meira-Machado

Title: Generating Multi-State Survival Data
Author: Artur Araujo, Luis Meira-Machado

Title: R Shiny App for Creating Simplified Trial Summary (TS) Domain
Description: Make it easy to create simplified trial summary (TS) domain

Title: Genetic Algorithm and Particle Swarm Optimization
Authors@R: c(person("Bogdan", "Potanin", email = "bogdanpotanin@gmail.com", role = c("aut", "cre", "ctb")))
Description: Implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) <doi:10.1007/s11042-020-10139-6> and M. Clerc (2012) <https://hal.archives-ouvertes.fr/hal-00764996>.