c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu",
comment = c(ORCID = "0000-0003-2716-1405")),
person("Franz", "Rendl", role = c("ctb", "cph")))
Implements heuristics for the Quadratic Assignment Problem (QAP). Although, the QAP was introduced as a combinatorial optimization problem for the facility location problem in operations research, it also has many applications in data analysis. The problem is NP-hard and the package implements a simulated annealing heuristic.
Michael Hahsler [aut, cre, cph]
(<https://orcid.org/0000-0003-2716-1405>),
Franz Rendl [ctb, cph]

Estimates Quantile Absolute Prediction Error using bootstrap procedures. The residual, parametric and double bootstrap is used.

Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added.

lme4, Matrix, mvtnorm, plyr, dplyr, matrixcalc, future.apply
Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap algorithms are used. Functions using the parallel computing for bootstrapping and Monte Carlo analyses have been added.

Performing and documenting scientific quality assurance of
meteorological data.
R (>= 2.6.1), ncdf, gplots, XML, gdata, moments, boot

Performing and documenting scientific quality assurance of meteorological data.

This package delivers some functions to provide a
scientific quality assurance of meteorological data.
R (>= 2.6.1), ncdf4, gplots, XML, gdata, moments, boot, fields

R (>= 2.6.1), ncdf, gplots, XML, gdata, moments, boot, fields

This package delivers some functions to provide a scientific quality assurance of meteorological data.

Functions for a scientific quality assurance of meteorological data.
ncdf4, gplots, XML, gdata, moments, boot, fields

Quantile Regression for Binary Longitudinal Data
Ayush Agarwal [aut, cre], Dootika Vats [ctb]
Implements the Bayesian quantile regression model for binary longitudinal data
(QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>.
The model handles both fixed and random effects and implements both a blocked
and an unblocked Gibbs sampler for posterior inference.
Rcpp, stats, grDevices, graphics, mcmcse, stableGR, RcppDist,
knitr, rmarkdown