Imports: data.table, igraph, magrittr, plyr, parallel, visNetwork

Title: Complete Functional Regulation Analysis
Authors@R: person("Rune", "Matthiesen", email = "runem2009@gmail.com", role = c("aut", "cre"))
Description: Calculates complete functional regulation analysis and visualize the results in a single heatmap. The provided example data is for biological data but the methodology can be used for large data sets to compare quantitative entities that can be grouped. For example, a store might divide entities into cloth, food, car products etc and want to see how sales changes in the groups after some event. The theoretical background for the calculations are provided in New insights into functional regulation in MS-based drug profiling, Ana Sofia Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports, <doi:10.1038/srep18826>.

Description: Calculates complete functional regulation analysis and visualize
the results in a single heatmap. The provided example data is for biological
data but the methodology can be used for large data sets to compare quantitative
entities that can be grouped. For example, a store might divide entities into
cloth, food, car products etc and want to see how sales changes in the groups
after some event. The theoretical background for the calculations are provided
in New insights into functional regulation in MS-based drug profiling, Ana Sofia
Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports <doi:10.1038/srep18826>.
Imports: gplots, grid, methods, parallel, stats, stringr

Description: Copula based imputation method. A semiparametric imputation procedure for missing multivariate data based on conditional copula specifications.
Packaged: 2014-03-01 17:02:56 UTC; francesca.dilascio2

Maintainer: Francesca Marta Lilja Di Lascio
Packaged: 2016-07-31 16:04:42 UTC; FDiLascio

Packaged: 2016-07-31 20:24:08 UTC; FDiLascio

Packaged: 2019-06-28 22:04:58 UTC; Administrator

Author: Antonio Canale [aut, cre], Daniele Durante [ctb], Arianna Falcioni [aut], Luisa Galtarossa [aut], Tommaso Rigon [ctb]
Description: Posterior inference under the convex mixture regression (CoMiRe) models introduced by Canale, Durante, and Dunson (2018) <doi:10.1111/biom.12917>.
Imports: Rcpp (>= 1.0.5), KernSmooth, NonpModelCheck, ggplot2, gtools,
mvtnorm, splines2 (>= 0.3.1), truncnorm, rlang
LinkingTo: RcppArmadillo, Rcpp (>= 1.0.5)