mirt (>= 1.26.3), AER, MASS, faraway, mlogit, ordinal, pscl,
knitr, testthat

CompQuadForm, mvtnorm, lavaan (>= 0.6-3), sandwich
mirt (>= 1.26.3), AER, MASS, faraway, mlogit, ordinal, pscl,
knitr, rmarkdown, testthat

CompQuadForm, mvtnorm, lavaan (>= 0.6-6), sandwich

A Collection of Nonparametric Hypothesis Tests
Contains the following 5 nonparametric hypothesis tests:
The Sign Test,
The 2 Sample Median Test,
Miller's Jackknife Procedure,
Cochran's Q Test, &
The Stuart-Maxwell Test.

Nonparametric Methods for Measuring Efficiency and Productivity
Dong-hyun Oh <oh.donghyun77@gmail.com>, Dukrok Suh
<dukrok@gmail.com>
R (>= 1.8.0), lpSolve, gdata, Hmisc, Design, geometry, psych,
pwt
This package contains functions for measuring efficiency
and productivity of decision making units (DMUs) under the
framework of Data Envelopment Analysis (DEA) and its
variations.

Dong-hyun Oh <oh.donghyun77@gmail.com>,
Dukrok Suh <dukrok@kist.re.kr>
Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.

R (>= 1.8.0), lpSolve, gdata, Hmisc, rms, geometry, psych, pwt

Dong-hyun Oh <oh.donghyun77@gmail.com>, with Dukrok Suh

person(given = "Erik-Cristian",
family = "Seulean",
role = c("aut", "cre"),
email = "erikseulean@gmail.com",
comment = c(ORCID = "https://orcid.org/0000-0002-1444-1574"))
Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.
Erik-Cristian Seulean [aut, cre]
(<https://orcid.org/0000-0002-1444-1574>)

Stratification and matching by the propensity score
This package offers a comprehensive data analysis if
stratification and matching by the propensity score is done.
Several functions are implemented, starting from the selection
of the propensity score model up to estimating propensity score
based treatment or exposure effects. All functions can be
applied separately as well as combined.