Introduction: History of monotone regression | A general isotone optimization framework | PAVA and active set algorithm | Special cases for active set optimization | Package description and examples | Discussion
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:
isotone_1.1-2.tar.gz
isotone_1.1-2.zip(r-4.7)isotone_1.1-2.zip(r-4.6)isotone_1.1-2.zip(r-4.5)
isotone_1.1-2.tgz(r-4.6-any)isotone_1.1-2.tgz(r-4.5-any)
isotone_1.1-2.tar.gz(r-4.7-any)isotone_1.1-2.tar.gz(r-4.6-any)
isotone_1.1-2.tgz(r-4.6-emscripten)
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:
Last updated from:bd3f6cfe22. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 107 | ||
| source / vignettes | OK | 165 | ||
| linux-release-x86_64 | OK | 107 | ||
| macos-release-arm64 | OK | 78 | ||
| macos-oldrel-arm64 | OK | 80 | ||
| windows-devel | OK | 81 | ||
| windows-release | OK | 60 | ||
| windows-oldrel | OK | 62 | ||
| wasm-release | OK | 88 |
Exports:activeSetaSolverdSolvereSolverfSolvergpavahSolveriSolverlfSolverlsSolvermregnnmregnnMmregnnPmSolveroSolverpSolversSolverweighted.fractileweighted.median
Dependencies:nnls
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 |
