LinkingTo: Rcpp (>= 0.12.6), RcppEigen (>= 0.3.2.9.0), RcppArmadillo

Packaged: 2017-11-23 19:09:52 UTC; Pieter Segaert

Packaged: 2018-07-01 16:58:49 UTC; Pieter Segaert

Packaged: 2018-07-07 09:08:31 UTC; Pieter Segaert

Title: Markov Random Field Structure Estimator
Description: A Markov random field structure estimator that uses a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion (Frondana, 2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.
Author: Rodrigo Carvalho [aut, cre], Florencia Leonardi [rev, ths]

Description: Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).
Authors@R: c(person("Rodrigo Carvalho", role = c("aut", "cre"), email = "rodrigorsdc@gmail.com"), person("Florencia Leonardi", role = c("rev", "ths")))

Description: Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion (Frondana, 2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm based on information theory (Bresler, 2015).

Title: Simulate with 'mrgsolve' in Parallel
Description: Simulation from an 'mrgsolve'
<https://cran.r-project.org/package=mrgsolve> model using a parallel backend.
Input data sets are split (chunked) and simulated in parallel using

Imports: parallel, dplyr, future, future.apply, callr, fst
Suggests: testthat, arrow, qs, knitr, rmarkdown

Title: Sensitivity Analysis with 'mrgsolve'
Authors@R: c(person("Kyle", "Baron", "", "kylebtwin@imap.cc", c("aut", "cre")))
Description: Perform sensitivity analysis on ordinary differential equation
based models, including ad-hoc graphical analyses based on structured
sequences of parameters as well as local sensitivity analysis. Functions
are provided for creating inputs, simulating scenarios and plotting outputs.
Imports: withr, purrr, dplyr, assertthat, rlang, ggplot2, tidyselect,
Collate: 'utils.R' 'parseq.R' 'sens.R' 'AAA.R' 'lsa.R' 'sens_each.R'