Package: isotone 1.1-2

Patrick Mair

isotone: Active Set and Generalized PAVA for Isotone Optimization

Contains two main functions: one for solving general isotone regression problems using the pool-adjacent-violators algorithm (PAVA); another one provides a framework for active set methods for isotone optimization problems with arbitrary order restrictions. Various types of loss functions are prespecified.

Authors:Patrick Mair [aut, cre], Jan De Leeuw [aut], Kurt Hornik [aut]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
isotone/json (API)

# Install 'isotone' in R:

install.packages('isotone', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://r-forge.r-project.org/projects/psychor

Datasets:

mendota - Number of freezing days at Lake Mendota
pituitary - Size of pituitary fissue
posturo - Repeated posturographic measures

On CRAN:

6.87 score 14 packages 99 scripts 1.5k downloads 2 mentions 19 exports 1 dependencies

Last updated from:bd3f6cfe22. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	103
source / vignettes	OK	144
linux-release-x86_64	OK	97
macos-release-arm64	OK	81
macos-oldrel-arm64	OK	85
windows-devel	OK	79
windows-release	OK	65
windows-oldrel	OK	60
wasm-release	OK	85

Exports:activeSet aSolver dSolver eSolver fSolver gpava hSolver iSolver lfSolver lsSolver mregnn mregnnM mregnnP mSolver oSolver pSolver sSolver weighted.fractile weighted.median

Dependencies:nnls

Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods

Rendered fromisotone.Rnwusingutils::Sweave

Last update: 2015-07-24
Started: 2015-07-24

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Active Set Methods for Isotone Optimization	activeSet print.activeset summary.activeset
Asymmetric Least Squares	aSolver
Absolute Value Norm	dSolver
L1 approximation	eSolver
User-Specified Loss Function	fSolver
Generalized Pooled-Adjacent-Violators Algorithm (PAVA)	gpava plot.pava print.pava
Huber Loss Function	hSolver
SILF Loss	iSolver
General Least Squares Loss Function	lfSolver
Least Squares Loss Function	lsSolver
Number of freezing days at Lake Mendota	mendota
Regression with Linear Inequality Restrictions on Predicted Values	mregnn mregnnM mregnnP
Chebyshev norm	mSolver
Lp norm	oSolver
Size of pituitary fissue	pituitary
Repeated posturographic measures	posturo
Quantile Regression	pSolver
Negative Poisson Log-Likelihood	sSolver
Weighted Median	weighted.fractile
Weighted Median	weighted.median