q-order partial correlation graph search algorithm
Robert Castelo <robert.castelo@upf.edu>, Alberto Roverato <roverato@unimore.it>
the q-order partial correlation graph search algorithm, q-partial,
or qp, algorithm for short, is a robust procedure for structure
learning of undirected Gaussian graphical Markov models from
"small n, large p" data, that is, multivariate normal data coming
from a number of random variables p larger than the number of
multidimensional data points n as in the case of, e.g., microarray
data.

Robert Castelo <robert.castelo@upf.edu>, Alberto Roverato <alberto.roverato@unibo.it>

Robert Castelo <robert.castelo@upf.edu>, Alberto Roverato
<alberto.roverato@unibo.it>
This package is deprecated and it is now only a stub for
the newer version called qpgraph available through the
Bioconductor project. The q-order partial correlation graph
search algorithm, q-partial, or qp, algorithm for short, is a
robust procedure for structure learning of undirected Gaussian
graphical Markov models from "small n, large p" data, that is,
multivariate normal data coming from a number of random
variables p larger than the number of multidimensional data
points n as in the case of, e.g., microarray data.

Noncompartmental Pharmacokinetic Analysis by qPharmetra
c(person("Jan", "Huisman", role = c("aut", "cre"),
email = "jan.huisman@qpharmetra.com"),
person("Koen", "Jolling", role = "ctb"),
person("Krina", "Mehta", role = "ctb"),
person("Tim", "Bergsma", role = "ctb"))
Jan Huisman [aut, cre],
Koen Jolling [ctb],
Krina Mehta [ctb],
Tim Bergsma [ctb]
Computes noncompartmental pharmacokinetic parameters
for drug concentration profiles.  For each profile, data
imputations and adjustments are made as necessary and
basic parameters are estimated. Supports single dose, multi-dose,
and multi-subject data.  Supports steady-state calculations
and various routes of drug administration. See ?qpNCA and vignettes.
Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8),
Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and
Gibaldi and Perrier (1982, ISBN:978-0824710422).
rmarkdown, markdown, covr, testthat (>= 2.1.0)
dplyr(>= 0.7.0), tidyr (>= 0.8.2), magrittr, ggplot2, knitr