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stepR (version 2.1-0)

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

241

Version

2.1-0

License

GPL-3

Maintainer

Pein Florian

Last Published

August 4th, 2020

Functions in stepR (2.1-0)

MRC.asymptotic.dyadic

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

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

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

Computation of the multiscale statistic

Bounds based on MRC

BesselPolynomial

Bessel Polynomials

Continuous time Markov chain

Compare fit blockwise with ground truth

Compute Multiresolution Criterion

Computation of the bounds

Family of distributions

parametricFamily

Parametric families

Digital filters

Interval systems

monteCarloSimulation

Monte Carlo simulation

Critical values

Neighbouring integers

Confidence intervals for jumps and confidence bands for step functions

Reconstruct filtered piecewise constant functions with noise

Solution path of step-functions

Jump estimation under restrictions

Piecewise constant multiscale inference

Multiscale Change-Point Inference

Robust standard deviation estimate

Fitted step function

Forward selection of candidate jumps

Piecewise constant regression with SMUCE

Automatic selection of number of jumps

testSmallScales

Test Small Scales

TRANSIT algorithm for detecting jumps