Clustering that splits data into Gaussian type clusters. The implementation allows the simultaneous use of various type Gaussian mixture models, performs the reduction of unnecessary clusters and it's able to discover new groups. Based on Spurek, P. and Tabor, J. (2014) <doi:10.1016/j.patcog.2014.03.006>.

CRISPR Screen and Gene Expression Differential Analysis
Provides analytical methods for analyzing CRISPR screen data
at different levels of gene expression. Multi-component normal mixture models
and EM algorithms are used for modeling.
Lianbo Yu [aut, cre],
Kevin R. Coombes [aut]

c(person("Lianbo","Yu",role=c("aut","cre"),
email="Lianbo.Yu@osumc.edu"),
person("Yue","Zhao",role="aut"),
person(c("Kevin","R."),"Coombes",role="aut"),
person("Lang","Li",role="aut"))
stats, mixtools, ggplot2, dplyr, ggsci, ggridges, ggprism
Lianbo Yu [aut, cre],
Yue Zhao [aut],
Kevin R. Coombes [aut],
Lang Li [aut]

Simple and Efficient Pipeline for Electronic Health Record
Annotation
Streamlined annotation pipeline for collection and aggregation of time-to-event data in retrospective clinical studies. 'CEDARS' aims to systematize and accelerate the review of electronic health record (EHR) corpora. It accomplishes those goals by deploying natural language processing as a tool to assist detection and characterization of clinical events by human abstractors. The online user manual presents the necessary steps to install 'CEDARS', process EHR corpora and obtain clinical event dates: <https://cedars.io>.
person("Simon", "Mantha", email = "smantha@cedars.io", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4277-5261"))
fastmatch, jsonlite, mongolite, parallel, readr, shiny,
udpipe, utils

Model building, surrogate model
based optimization and Efficient Global Optimization in combinatorial
or mixed search spaces.

MASS, stats, DEoptim, graphics, quadprog, Matrix, expm

MASS, stats, DEoptim, graphics, quadprog, Matrix, methods,
ParamHelpers, fastmatch

Climate and Ecological Niche Factor Analysis
Tools for climate- and ecological-niche factor analysis of spatial data, including methods for visualization of spatial variability of species sensitivity, exposure, and vulnerability to climate change. Processing of large files and parallel methods are supported. Ecological-niche factor analysis is described in Hirzel et al. (2002) <doi:10.2307/3071784> and Basille et al. (2008) <doi:10.1016/j.ecolmodel.2007.09.006>.
'CENFA.R' 'GLcenfa-class.R' 'GLcenfa.R' 'GLdeparture-class.R'
'GLdeparture.R' 'RcppExports.R' 'brStick.R' 'calc.R'
'climdat.fut.R' 'climdat.hist.R' 'cnfa-class.R' 'cnfa.R'
'covij.R' 'departure-class.R' 'departure.R' 'enfa-class.R'
'enfa.R' 'exposure_map.R' 'names.R' 'parCov.R' 'parScale.R'
'scatter.R' 'sensitivity_map.R' 'slots.R' 'stretchPlot.R'
'tree-data.R' 'vulnerability-class.R' 'vulnerability.R'
'vulnerability_map.R'