Generate Samples from Multivariate Truncated Normal
Distributions
c(person("Zhenyu", "Zhang", email = "zhangzhenyusa@gmail.com", role = c("aut", "cre")),
person("Andrew", "Chin", email = "achin23@jhu.edu", role = "aut"),
person("Akihiko", "Nishimura", email = "akihiko4@gmail.com", role = "aut"),
person("Marc A.", "Suchard", email = "msuchard@ucla.edu", role = "aut"),
person("John W.", "Ratcliff et al.", role = c("cph", "ctb"),
comment = "authors and copyright holders of see2neon.h under an MIT license"))
Efficient sampling from high-dimensional truncated Gaussian
distributions (so comes the package name hdtg). Techniques include zigzag Hamiltonian
Monte Carlo as in Akihiko Nishimura, Zhenyu Zhang and Marc A. Suchard (2021)
<arXiv:2104.07694>, and harmonic Hamiltonian Monte Carlo in Ari Pakman and Liam
Paninski (2014) <doi:10.1080/10618600.2013.788448>.
Rcpp, RcppParallel, RcppXsimd, mgcv, stats, Rdpack
Zhenyu Zhang [aut, cre],
Andrew Chin [aut],
Akihiko Nishimura [aut],
Marc A. Suchard [aut],
John W. Ratcliff et al. [cph, ctb] (authors and copyright holders of
see2neon.h under an MIT license)

Efficient sampling from high-dimensional truncated Gaussian
distributions, or multivariate truncated normal (MTN). Techniques include
zigzag Hamiltonian Monte  Carlo as in Akihiko Nishimura, Zhenyu Zhang and
Marc A. Suchard (2021) <arXiv:2104.07694>, and  harmonic Monte in Ari Pakman
and Liam Paninski (2014) <doi:10.1080/10618600.2013.788448>.

Compose Sentences to Describe Comparisons
person(
given = "Jake",
family = "Riley",
role = c("aut", "cre"),
email = "rjake@sas.upenn.edu"
)
Create dynamic, data-driven text. Given two values, a list of
talking points is generated and can be combined using string
interpolation. Based on the 'glue' package.
ggplot2, knitr, nycflights13, rmarkdown, testthat
dplyr, glue, lubridate, purrr, rlang, tibble, tidyr

c(
person("Levi", "Thatcher", email = "levi.thatcher@healthcatalyst.com", role = c("aut", "cre")),
person("Mike", "Mastanduno", email = "mike.mastanduno@healthcatalyst.com", role = "aut"),
person("Taylor", "Miller", email = "taylor.miller@healthcatalyst.com", role = "aut"),
person("Taylor", "Larsen", email = "taylor.larsen@healthcatalyst.com", role = "aut"))
A machine learning toolbox tailored to healthcare data.
Aids in data cleaning, model development, hyperparameter tuning, and model
deployment in a production SQL environment. Algorithms currently supported
are Lasso, Random Forest, and Linear Mixed Model.
caret, data.table, doParallel, e1071, grpreg, lme4, lubridate,
pROC, R6, ranger, ROCR, RODBC
Levi Thatcher [aut, cre],
Mike Mastanduno [aut],
Taylor Miller [aut],
Taylor Larsen [aut]