R Shiny App for Creating Simplified Trial Summary (TS) Domain
Make it easy to create simplified trial summary (TS) domain
based on FDA FDA guide
<https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.

Genetic Algorithm and Particle Swarm Optimization
c(person("Bogdan", "Potanin", email = "bogdanpotanin@gmail.com", role = c("aut", "cre", "ctb")))
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>.

R based genetic algorithm for binary and floating point
chromosomes.

Global ENvironmental ASsessment Information System (GENASIS)
computational tools
Jiri Kalina, Jana Klanova, Ladislav Dusek, Tom Harner, Jana Boruvkova, Jiri Jarkovsky
genasis package contains methods for air pollution assessment. Concerned on persistent organic pollutants, the package allows to compute trends of their concentrations, compare different datasets and estimate relations between values from active and passive air samplers.

Helgem de Souza Ribeiro Martins[aut, cre], Anderson Ribeiro Duarte[aut]
Helgem de Souza Ribeiro Martins <helgem.souza@gmail.com>
Provides a function that generates customized correlation matrix based on
limit values and proportions for intervals composed by its limits. Can also generate
random correlation matrices, matrices with low, medium and high correlations, which
low, medium and high thresholds are user-defined.

Provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.

Engines for cross-validation of many types of regression and class prediction models are provided. These engines include built-in support for 'glmnet', 'lars', 'plus', 'MASS', 'rpart', 'C50' and 'randomforest'. It is easy for the user to add other regression or classification algorithms. The 'parallel' package is used to improve speed. Several data generation algorithms for problems in regression and classification are provided.
lars, glmnet, plus, C50, randomForest, e1071, rpart, MASS,
class, nnet