Title: Generalized Multistate Simulation Model
Maintainer: Luisa Salazar Vizcaya <luisa.salazar@ispm.unibe.ch>
Imports: MASS, methods, msm, plyr, graphics, stats, utils, data.table
Description: Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.

Description: Simulate and analyze multistate models with general hazard
functions. gems provides functionality for the preparation of hazard functions
and parameters, simulation from a general multistate model and predicting future
events. The multistate model is not required to be a Markov model and may take
the history of previous events into account. In the basic version, it allows
to simulate from transition-specific hazard function, whose parameters are

Maintainer: Luisa Salazar Vizcaya <luisapaola.salazarvizcaya@insel.ch>

Author: Gert van Valkenhoef, Joel Kuiper
Maintainer: Gert van Valkenhoef <g.h.m.van.valkenhoef@rug.nl>
Description: An R package for performing network meta-analyses (mixed
Suggests: rjags (>= 3-0), BRugs (>= 0.8), R2WinBUGS (>= 2.1)

Suggests: rjags (>= 3-0), BRugs (>= 0.8), R2WinBUGS (>= 2.1), testthat

Description: An R package for performing network meta-analyses (mixed treatment comparisons).

Title: Network Meta-Analysis Using Bayesian Methods
Description: Network meta-analyses (mixed treatment comparisons) in the Bayesian framework. You need either BUGS or JAGS to use this package, preferably JAGS (through the rjags package). Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations.

Description: Network meta-analyses (mixed treatment comparisons) in the Bayesian
framework using JAGS. Includes methods to assess heterogeneity and
inconsistency, and a number of standard visualizations.
Imports: igraph (>= 1.0), meta (>= 2.1), plyr (>= 1.8), graphics,
grDevices, stats, utils, grid, rjags (>= 3-0), truncnorm
Suggests: testthat (>= 0.8), Matrix, XML (>= 3.6)
Collate: 'anohe.R' 'blobbogram.R' 'code.R' 'data.R' 'deviance.R'