c(
person("John", "Marden", role = c("aut", "cph"),
email = "jimarden@illinois.edu"),
person("James", "Balamuta", role = c("cre", "ctb", "com"),
email = "james.balamuta@gmail.com",
comment = c(ORCID = "0000-0003-2826-8458"))
)
Multivariate Analysis methods and data sets used
in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
This also serves as a companion package for the
STAT 571: Multivariate Analysis course offered by the Department of Statistics
at the University of Illinois at Urbana-Champaign ('UIUC').
John Marden [aut, cph],
James Balamuta [cre, ctb, com]
(<https://orcid.org/0000-0003-2826-8458>)

Multi-state Path-Dependent Models in Discrete Time
Ross Boylan <ross@biostat.ucsf.edu> and Peter Bacchetti
<peter@biostat.ucsf.edu>, building on the work of Christopher
H. Jackson <chris.jackson@imperial.ac.uk>. Thorsten Ottosen
<nesotto@cs.auc.dk> for the ptr_container library. Gennadiy
Rozental <rogeeff@fusemail.com> for the Boost Test Library. And
the authors of other Boost libraries used by the previous two.
Functions for fitting path-dependent (non-Markov)
multi-state models to categorical processes observed at
arbitrary times, optionally with misclassified responses, and
covariates on transition or misclassification rates.  Uses
discrete-time approximation.  Based on the Jackson's msm
package v 0.3.1, with an interface as compatible as possible.
allGenerics.R checkPoint.R mspathCoefficients.R
mspathCalculator.R mspathDistributedCalculator.R mspath.R
readingError.R runTime.R subset.R trial.R utils.R zzz.R

Samuel Gerber <sgerber@cs.utah.edu>, Kristi Potter
<kpotter@sci.utah.edu>, Oliver Ruebel <ruebel1@llnl.gov>
Package for discrete Morse-Smale complex approximation
based on kNN graph. The Morse-Smale complex provides a
decomposition of the domain. This package provides methods to
compute a hierarchical sequence of Morse-Smale complicies and
tools that exploit this domain decomposition for regression and
visualization of scalar functions.
Morse-Smale approximation, regression and visualization
R (>= 1.9.0), stats, class, glmnet, stats4, e1071, rgl,
RColorBrewer, colorspace