"ggdist-package.R" "util.R" "abstract_geom.R"
"abstract_stat.R" "abstract_stat_slabinterval.R"
"binning_methods.R" "curve_interval.R" "cut_cdf_qi.R" "data.R"
"distributions.R" "draw_key_slabinterval.R" "geom.R"
"geom_slabinterval.R" "geom_dotsinterval.R" "geom_interval.R"
"geom_lineribbon.R" "geom_pointinterval.R" "geom_slab.R"
"guide_rampbar.R" "lkjcorr_marginal.R" "parse_dist.R"
"point_interval.R" "position_dodgejust.R" "rd.R"
"rd_slabinterval.R" "rd_lineribbon.R" "scale_colour_ramp.R"
"scale_thickness.R" "scale_.R" "stat.R" "stat_slabinterval.R"
"stat_dotsinterval.R" "stat_pointinterval.R" "stat_interval.R"
"stat_lineribbon.R" "student_t.R" "testthat.R" "theme_ggdist.R"
"tidy_format_translators.R" "deprecated.R"

A 'ggplot2' Extension for Plotting Unimodal Distributions
c(
person("Joseph M.", "Burling",
email = "josephburling@gmail.com",
role = c("aut", "cre")))
The 'ggdistribute' package is an extension for plotting posterior or other
types of unimodal distributions that require overlaying information about a
distribution's intervals. It makes use of the 'ggproto' system to extend
'ggplot2', providing additional "geoms", "stats", and "positions."
The extensions integrate with existing 'ggplot2' layer elements.
data.table, ggplot2, tibble, magrittr, grDevices
knitr, testthat, rmarkdown, viridisLite, extrafont

data.table, ggplot2, tibble, magrittr, grDevices, dplyr

data.table, ggplot2 (>= 3.0.0), tibble, magrittr, grDevices,
dplyr

Dynamic Model of Choice with Parallel Computation, and C++
Capabilities
Yi-Shin Lin [aut, cre], Andrew Heathcote [aut]
A fast engine for computing hierarchical Bayesian model
implemented in the Dynamic Model of Choice.

Hierarchical Bayesian modelling. The 'ggdmc' package provides tools designed for fitting cognitive models, using population-based Markov Chain Monte Carlo (pMCMC) and fast C++ libraries. The paper by Heathcote, Lin, Reynolds, Strickland, Gretton, and Matzke (2018, <doi:10.3758/s13428-018-1067-y>) describes the early prototype of the package (by the version 0.4.2). The paper accompanied with the revamped latest version (> 0.2.4.5) is under preparation Lin, Strickland, Reynold, and Heathcote (2018) and a further paper regarding the new pMCMC sampling modified based on Hu and Tsai's (2010) work is also under preparation. See 'citation("ggdmc")' for details.
Rcpp (>= 0.12.10), coda, rtdists, ggmcmc (>= 0.7.3), stats,
utils, data.table (>= 1.10.4), tmvtnorm, ggplot2, matrixStats