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isotone (version 1.1-1)

Active Set and Generalized PAVA for Isotone Optimization

Description

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.

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Version

Install

install.packages('isotone')

Monthly Downloads

801

Version

1.1-1

License

GPL-2

Maintainer

Last Published

February 22nd, 2023

Functions in isotone (1.1-1)

sSolver

Negative Poisson Log-Likelihood
pituitary

Size of pituitary fissue
oSolver

Lp norm
activeSet

Active Set Methods for Isotone Optimization
mendota

Number of freezing days at Lake Mendota
gpava

Generalized Pooled-Adjacent-Violators Algorithm (PAVA)
aSolver

Asymmetric Least Squares
lfSolver

General Least Squares Loss Function
hSolver

Huber Loss Function
iSolver

SILF Loss
fSolver

User-Specified Loss Function
eSolver

L1 approximation
dSolver

Absolute Value Norm
weighted.fractile

Weighted Median
posturo

Repeated posturographic measures
weighted.median

Weighted Median
mSolver

Chebyshev norm
mregnn

Regression with Linear Inequality Restrictions on Predicted Values
lsSolver

Least Squares Loss Function
pSolver

Quantile Regression