Title: Bayesian Tensor Factorization Linked to External Data
Authors@R: person("Nathan", "Lazar", email = "nathan.lazar@gmail.com", role = c("aut", "cre"))
Description: BaTFLED is a machine learning algorithm designed to make predictions and determine interactions in data that varies along three independent modes. For example BaTFLED was developed to predict the growth of cell lines when treated with drugs at different doses. The first mode corresponds to cell lines and incorporates predictors such as cell line genomics and growth conditions. The second mode corresponds to drugs and incorporates predictors indicating known targets and structural features. The third mode corresponds to dose and there are no dose-specific predictors (although the algorithm is capable of including predictors for the third mode if present). See 'BaTFLED3D_vignette.rmd' for a simulated example.
Collate: 'diagnostics.R' 'CP_model.R' 'Tucker_model.R' 'exp_var.R'
'safe_log.R' 'safe_prod.R' 'show_mat.R' 'test.R' 'test_CP.R'
Imports: foreach, R6, iterators, rTensor, RColorBrewer
Suggests: doMC, doParallel, knitr, rmarkdown

Collate: 'diagnostics.R' 'CP_model.R' 'diagonal.R' 'Tucker_model.R'
'mk_model.R' 'mk_toy.R' 'mult_3d.R' 'nrmse.R' 'plot_preds.R'
'safe_prod.R' 'show_mat.R' 'test.R' 'test_CP.R' 'test_Tucker.R'

Suggests: doMC, doParallel, knitr, rmarkdown, testthat

Title: Modeling Framework for Cellular Communities in their
Authors@R: c( person("Eugen", "Bauer", , "eugen.bauer@uni.lu", role = c("aut")),
person("Johannes", "Zimmermann", , "j.zimmermann@iem.uni-kiel.de", role = c("aut", "cre")))
Description: Can be used for simulation of organisms living in
communities. Each organism is represented individually and genome scale
metabolic models determine the uptake and release of compounds. Biological
processes such as movement, diffusion, chemotaxis and kinetics are available
Imports: igraph, methods, utils, stats, graphics, Rcpp
Suggests: glpkAPI, sybilSBML, knitr, rmarkdown

Imports: methods, utils, stats, graphics, Rcpp