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stepR (version 2.1-9)

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, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) and HSMUCE, heterogeneous SMUCE, (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

233

Version

2.1-9

License

GPL-3

Maintainer

Pein Florian

Last Published

November 13th, 2023

Functions in stepR (2.1-9)

monteCarloSimulation

Monte Carlo simulation

Interval systems

Digital filters

Confidence intervals for jumps and confidence bands for step functions

Family of distributions

Reconstruct filtered piecewise constant functions with noise

Critical values

Neighbouring integers

parametricFamily

Parametric families

Forward selection of candidate jumps

Automatic selection of number of jumps

Jump estimation under restrictions

Multiscale Change-Point Inference

Fitted step function

Robust standard deviation estimate

Solution path of step-functions

Piecewise constant regression with SMUCE

Piecewise constant multiscale inference

testSmallScales

Test Small Scales

TRANSIT algorithm for detecting jumps

MRC.asymptotic.dyadic

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

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

Continuous time Markov chain

BesselPolynomial

Bessel Polynomials

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

Compare fit blockwise with ground truth

Compute Multiresolution Criterion

Bounds based on MRC

Computation of the multiscale statistic

Computation of the bounds