modeling that must have been installed in order to use 'Rsmlx'.
Among other tasks, 'Rsmlx' provides a powerful tool for automatic PK model building, performs
Imports: MASS, RJSONIO, ggplot2, gridExtra, graphics, grDevices, utils,

Imports: tools, methods, graphics, grDevices, utils, stats, tcltk,
Collate: mlxConnectors.R bootstrap.R buildmlx.R confintmlx.R
setSettings.R testmlx.R zzz.R RsmlxTools.R pkbuild.R pkpopini.R

person( "Jonathan", "Chauvin", role = c("ctb"), email = "Jonathan.Chauvin@lixoft.com" ),
person( "Clémence", "Pinaud", role = c("ctb"), email = "Clemence.Pinaud@lixoft.com" ))
Imports: graphics, grDevices, utils, stats, MASS, ggplot2, gridExtra

'Monolix' <https://monolix.lixoft.com>. 'Monolix' is a software tool for nonlinear mixed effects
Imports: graphics, grDevices, utils, stats, MASS, ggplot2, gridExtra,
Collate: mlxConnectors.R bootstrap.R buildmlx.R buildVar.R buildAll.R
testmlx.R zzz.R RsmlxTools.R pkbuild.R pkpopini.R

testmlx.R zzz.R RsmlxTools.R readDatamlx.R pkbuild.R pkpopini.R

Author: Alexios Ghalanos and Stefan Theussl
Description: General Non-linear Optimization Using Augmented Lagrange

Depends: R (>= 2.10.0), stats, truncnorm

Depends: R (>= 2.10.0), stats, truncnorm, parallel

Description: General Non-linear Optimization Using Augmented Lagrange Multiplier Method

Description: General Non-linear Optimization Using Augmented Lagrange Multiplier Method.

Authors@R: c(person(given = "Peter", family = "Wittek", role = c("aut"), email = ""),
person(given = "Shichao", family = "Gao", role = c("cre"), email = "xgdgsc@gmail.com"))
Description: Somoclu is a cluster-oriented implementation of self-organizing maps. It 			     relies on MPI for distributing the workload, and it can be accelerated by 			     CUDA on a GPU cluster. A sparse kernel is also included, which is useful for 		     training maps on vector spaces generated in text mining processes.
Author: Peter Wittek [aut], Shichao Gao [cre]

Description: Somoclu is a massively parallel implementation of self-organizing maps.  It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal .