rms (>= 4.3-1), lattice, latticeExtra, ggplot2, Formula,
survival
Contains many functions useful for
monitoring and reporting the results of clinical trials and other
experiments in which treatments are compared.  LaTeX is
used to typeset the resulting reports, recommended to be in the
context of 'knitr'. The 'Hmisc', 'ggplot2', and 'lattice' packages are used
by 'greport' for high-level graphics.

rms (>= 4.4-1), lattice, latticeExtra, ggplot2, Formula,
survival, methods

rms (>= 5.0-0), lattice, latticeExtra, ggplot2, Formula,
survival, methods, data.table

Simple and Scalable Statistical Modelling in R
person("Nick", "Golding", role = c("aut", "cre"),
email = "nick.golding.research@gmail.com")
Write statistical models in R and fit them by MCMC on CPUs and GPUs, using Google TensorFlow (see <https://goldingn.github.io/greta> for more information).
Python (>= 2.7.0) with header files and shared
library; TensorFlow (>= 1.0.0; https://www.tensorflow.org/)
'package.R' 'overloaded.R' 'node_class.R' 'node_types.R'
'variable.R' 'probability_distributions.R' 'unknowns_class.R'
'greta_array_class.R' 'as_data.R' 'utils.R' 'distribution.R'
'operators.R' 'functions.R' 'transforms.R' 'structures.R'
'extract_replace_combine.R' 'dynamics_module.R' 'dag_class.R'
'greta_model_class.R' 'progress_bar.R' 'inference.R'
'samplers.R' 'install_tensorflow.R'
R6, tensorflow, reticulate, progress, coda
knitr, rmarkdown, DiagrammeR, bayesplot, lattice, testthat,
mvtnorm, MCMCpack, rmutil, extraDistr, truncdist

'package.R' 'utils.R' 'tf_functions.R' 'overloaded.R'
'node_class.R' 'node_types.R' 'variable.R'
'probability_distributions.R' 'unknowns_class.R'
'greta_array_class.R' 'as_data.R' 'distribution.R'
'operators.R' 'functions.R' 'transforms.R' 'structures.R'
'extract_replace_combine.R' 'dag_class.R' 'greta_model_class.R'
'progress_bar.R' 'samplers.R' 'inference.R'
'install_tensorflow.R' 'internals.R'

c(
person("Nick", "Golding", role = c("aut", "cre"),
email = "nick.golding.research@gmail.com",
comment = c(ORCID = "0000-0001-8916-5570")),
person("Simon", "Dirmeier", role = "ctb"),
person("Adam", "Fleischhacker", role = "ctb"),
person("Shirin", "Glander", role = "ctb"),
person("Martin", "Ingram", role = "ctb"),
person("Lee", "Hazel", role = "ctb"),
person("Tiphaine", "Martin", role = "ctb"),
person("Matt", "Mulvahill", role = "ctb"),
person("Michael", "Quinn", role = "ctb"),
person("David", "Smith", role = "ctb"),
person("Paul", "Teetor", role = "ctb"),
person("Jian", "Yen", role = "ctb")
)
Write statistical models in R and fit them by MCMC on CPUs and GPUs, using Google TensorFlow (see <https://greta-dev.github.io/greta> for more information).
Python (>= 2.7.0) with header files and shared
library; TensorFlow (>= 1.10; https://www.tensorflow.org/);
Tensorflow Probability (>=0.3.0;
https://www.tensorflow.org/probability/)
'package.R' 'utils.R' 'tf_functions.R' 'overloaded.R'
'node_class.R' 'node_types.R' 'variable.R'
'probability_distributions.R' 'mixture.R' 'joint.R'
'unknowns_class.R' 'greta_array_class.R' 'as_data.R'
'distribution.R' 'operators.R' 'functions.R' 'transforms.R'
'structures.R' 'extract_replace_combine.R' 'dag_class.R'
'greta_model_class.R' 'progress_bar.R' 'inference_class.R'
'samplers.R' 'optimisers.R' 'inference.R'
'install_tensorflow.R' 'internals.R' 'calculate.R'
'callbacks.R'
R6, tensorflow, reticulate, progress (>= 1.2.0), future, coda,
methods
knitr, rmarkdown, DiagrammeR, bayesplot, lattice, testthat,
mvtnorm, MCMCpack, rmutil, extraDistr, truncdist, tidyverse,
fields, MASS, abind
Nick Golding [aut, cre] (<https://orcid.org/0000-0001-8916-5570>),
Simon Dirmeier [ctb],
Adam Fleischhacker [ctb],
Shirin Glander [ctb],
Martin Ingram [ctb],
Lee Hazel [ctb],
Tiphaine Martin [ctb],
Matt Mulvahill [ctb],
Michael Quinn [ctb],
David Smith [ctb],
Paul Teetor [ctb],
Jian Yen [ctb]