Suggests: parallel, graph, Rgraphviz, lattice, gRain, ROCR
Author: Marco Scutari [aut, cre], Robert Ness [ctb]

Suggests: parallel, graph, Rgraphviz, lattice, gRain, ROCR, Rmpfr, gmp
This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB,

Description: Bayesian network structure learning, parameter learning and inference.
This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete,
Gaussian and conditional Gaussian networks, along with many score functions and
parameter estimation (maximum likelihood and Bayesian) and inference, conditional
probability queries, cross-validation, bootstrap and model averaging.
Development snapshots with the latest bugfixes are available from <http://www.bnlearn.com>.

Suggests: parallel, graph, Rgraphviz, igraph, lattice, gRain (>=
Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

Author: Marco Scutari [aut, cre], Tomi Silander [ctb], Robert Ness [ctb]

Title: Bayesian Network Meta-Analysis using 'JAGS'
Imports: rjags (>= 4-6), graphics, stats, utils, coda (>= 0.13),
person("Michael", "Seo", email = "swj8874@gmail.com", role = c("aut", "cre")),
person("Christopher", "Schmid", email = "christopher_schmid@brown.edu", role = "aut"))
Description: Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-based data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.

Description: Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.