Title: Datasets to Accompany Hollander, Wolfe, and Chicken NSM3
person("Grant", "Schneider", role = c("aut")),
person("Eric", "Chicken", role = c("aut")),
person("Rachel", "Becvarik", role = c("aut")),
person("Sejin", "Kim", email = "kim3@kenyon.edu", role=c("aut", "cre")))
Description: Designed to add datasets which are used in the Nonparametric Statistical Methods textbook, 3rd edition.

Title: Inference for Multiple Change-Points in Linear Models
Description: Implementation of Narrowest Significance Pursuit, a general and
flexible methodology for automatically detecting localised regions in data sequences
which each must contain a change-point (understood as an abrupt change in the
parameters of an underlying linear model), at a prescribed global significance level.
Narrowest Significance Pursuit works with a wide range of distributional assumptions
on the errors, and yields exact desired finite-sample coverage probabilities,
regardless of the form or number of the covariates. For details, see P. Fryzlewicz
Author: Piotr Fryzlewicz [aut, cre] (<https://orcid.org/0000-0002-9676-902X>)

Title: Nonparametric and Semiparametric Mixture Estimation
Description: Contains functions for maximum likelihood estimation of
nonparametric and semiparametric mixture models.

Description: Mainly for maximum likelihood estimation of nonparametric
and semiparametric mixture models, but can also be used
for fitting finite mixtures. The algorithms are

Package: nsprcomp
Authors@R: c( person("Sigg", "Christian", email =
"christian@sigg-iten.ch", role = c("aut", "cre")), person("R
Core team", role = "aut"))
Version: 0.2
Date: 2013-04-07
Title: Non-Negative Sparse PCA
Description: This package implements two methods for performing a
principal component analysis (PCA) with constraints, where
non-negativity and/or sparsity constraints are enforced on the
principal axes (PAs).  The function 'nsprcomp' computes one
principal component (PC) after the other. Each PA is optimized
such that the corresponding PC has maximum variance under the
given constraints. Then the data matrix is deflated using one
of several provided deflation methods, and the process is
repeated with the deflated data matrix to compute the next PC.
In contrast, the function 'nscumcomp' jointly computes all PCs
such that the cumulative variance is maximal, subject to an
upper bound on the divergence from orthonormality of the
rotation matrix.  Both functions have the same interface as the
'prcomp' function from the 'stats' package (plus some extra
parameters), and both return the result of the analysis as an
object of class 'nsprcomp', which inherits from 'prcomp'.
License: GPL (>= 2)
Imports: stats
Suggests: testthat, roxygen2, MASS
Collate: 'nsprcomp-package.r' 'nsprcomp.R' 'nscumcomp.R'
Packaged: 2013-04-16 18:24:35 UTC; chrsigg
Author: Sigg Christian [aut, cre], R Core team [aut]
Maintainer: Sigg Christian <christian@sigg-iten.ch>
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-04-16 21:28:42