Power calculations and Bayesian analysis of count distributions
and FECRT data using MCMC
Matthew Denwood <m.denwood@vet.gla.ac.uk> (funded as part of
the DEFRA VTRI project 0101).
R (>= 2.6), runjags (>= 0.9.5), coda, lattice, stats, utils
A set of functions to allow analysis of count data (such
as faecal egg count data) using Bayesian MCMC methods.  Returns
information on the possible values for mean count, coefficient
of variation and zero inflation (true prevalence) present in
the data.  A complete faecal egg count reduction test (FECRT)
model is implemented, which returns inference on the true
efficacy of the drug from the pre and post treatment data
provided, using non-parametric bootstrapping as well as using
Bayesian MCMC.  Functions to perform power analyses for faecal
egg counts (including FECRT) are also provided.  Requires Just
Another Gibbs Sampler (JAGS) for most functions (except power
analysis calculations), see:
http://www-fis.iarc.fr/~martyn/software/jags/.

Power Calculations and Bayesian Analysis of Count Distributions
and FECRT Data using MCMC
A set of functions to allow analysis of count data (such
as faecal egg count data) using Bayesian MCMC methods.  Returns
information on the possible values for mean count, coefficient
of variation and zero inflation (true prevalence) present in
the data.  A complete faecal egg count reduction test (FECRT)
model is implemented, which returns inference on the true
efficacy of the drug from the pre- and post-treatment data
provided, using non-parametric bootstrapping as well as using
Bayesian MCMC.  Functions to perform power analyses for faecal
egg counts (including FECRT) are also provided.

runjags (>= 2.0.1), rjags, coda, stats, utils

Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
c(
person(c("Eric", "J."), "Ward", role = c("aut", "cre"),
email = "eric.ward@noaa.gov"),
person(c("Sean", "C."), "Anderson", role = "aut"),
person(c("Luis", "A."), "Damiano", role = "aut"),
person(c("Mary", "E."), "Hunsicker,", role = "ctb"),
person(c("Mike", "A."), "Litzow", role = "ctb"),
person("Trustees of", "Columbia University", role = "cph"))
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic
factor analysis is a dimension reduction tool for multivariate time series.
'bayesdfa' extends conventional dynamic factor models in several ways.
First, extreme events may be estimated in the latent trend by modeling
process error with a student-t distribution. Second, autoregressive and
moving average components can be optionally included. Third, the estimated
dynamic factors can be analyzed with hidden Markov models to evaluate
support for latent regimes.
rstan (>= 2.16.0), ggplot2 (>= 2.2.0), loo, dplyr (>= 0.7.0),
methods, rlang, reshape2
testthat, parallel, knitr, rmarkdown, MARSS
Eric J. Ward [aut, cre],
Sean C. Anderson [aut],
Luis A. Damiano [aut],
Mary E. Hunsicker, [ctb],
Mike A. Litzow [ctb],
Trustees of Columbia University [cph]