A light-weight package helps you track and visualize
the progress of parallel version of vectorized R functions (mc*apply).
The package works on *nix (Linux, Unix such as macOS) system only due to
the lack of fork() functionality, which is essential for mcapply, on Windows.

A light-weight package helps you track and visualize
the progress of parallel version of vectorized R functions (mc*apply).
The package works on *nix (Linux, Unix such as macOS) system only due to
the lack of fork() functionality, which is essential for mc*apply, on Windows.

Kevin Kuang (aut), Francesco Napolitano (ctb)

A light-weight package helps you track and visualize
the progress of parallel version of vectorized R functions (mc*apply).
Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to
the lack of fork() functionality, which is essential for mc*apply, on Windows.

Kevin Kuang (aut), Quyu Kong (ctb), Francesco Napolitano (ctb)

Following the method of Bailey et al., computes for a collection
of candidate models the probability of backtest overfitting, the
performance degradation and probability of loss, and the stochastic
dominance.
PerformanceAnalytics, foreach, grid, latticeExtra, testthat,
doParallel, knitr

PerformanceAnalytics, grid, testthat, doParallel, parallel,
knitr, spelling

Probabilities for Bivariate Normal Distribution
Computes probabilities of the bivariate normal distribution
in a vectorized R function (Drezner & Wesolowsky, 1990,
<doi:10.1080/00949659008811236>).

Stan Models for the Pairwise Comparison Factor Model
c(person(given = c("Joshua", "N."),
family = "Pritikin",
role = c("aut", "cre"),
email = "jpritikin@pobox.com",
comment = c(ORCID = "0000-0002-9862-5484")),
person(given = c("Daniel C."), family = "Furr", role="ctb", email = "danielcfurr@berkeley.edu"),
person("Trustees of Columbia University", role="cph"))
Provides convenience functions and pre-programmed Stan models
related to the pairwise comparison factor model. Its purpose is to make fitting
pairwise comparison data using Stan easy.
rstan (>= 2.18), reshape2, mvtnorm, igraph
knitr, rmarkdown, testthat, shiny, ggplot2, covr, qgraph
Joshua N. Pritikin [aut, cre] (<https://orcid.org/0000-0002-9862-5484>),
Daniel C. Furr [ctb],
Trustees of Columbia University [cph]