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stepR (version 2.0-3)

Multiscale Change-Point Inference

Description

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE (K. Frick, A. Munk and H. Sieling, 2014) and HSMUCE (F. Pein, H. Sieling and A. Munk, 2017) . In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.

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Version

Install

install.packages('stepR')

Monthly Downloads

253

Version

2.0-3

License

GPL-3

Maintainer

Pein Florian

Last Published

January 27th, 2019

Functions in stepR (2.0-3)

family

Family of distributions
monteCarloSimulation

Monte Carlo simulation
transit

TRANSIT algorithm for detecting jumps
dfilter

Digital filters
stepFit

Piecewise constant multiscale inference
stepbound

Jump estimation under restrictions
stepblock

Step function
computeBounds

Computation of the bounds
stepR-package

Multiscale Change-Point Inference
critVal

Critical values
neighbours

Neighbouring integers
jsmurf

Reconstruct filtered piecewise constant functions with noise
parametricFamily

Parametric families
stepcand

Forward selection of candidate jumps
jumpint

Confidence intervals for jumps and confidence bands for step functions
stepfit

Fitted step function
penalty

Penalties
intervalSystem

Interval systems
sdrobnorm

Robust standard deviation estimate
steppath

Solution path of step-functions
stepsel

Automatic selection of number of jumps
smuceR

Piecewise constant regression with SMUCE
MRC.asymptotic

"Asymptotic" values of the MRC statistic (all intervals)
bounds

Bounds based on MRC
BesselPolynomial

Bessel Polynomials
compareBlocks

Compare fit blockwise with ground truth
MRC

Compute Multiresolution Criterion
MRC.1000

Values of the MRC statistic for 1,000 observations (all intervals)
computeStat

Computation of the multiscale statistic
contMC

Continuous time Markov chain
MRC.asymptotic.dyadic

"Asymptotic" values of the MRC statistic (dyadic intervals)