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stepR (version 2.1-10)

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

309

Version

2.1-10

License

GPL-3

Maintainer

Pein Florian

Last Published

October 18th, 2024

Functions in stepR (2.1-10)

Neighbouring integers

Interval systems

Digital filters

monteCarloSimulation

Monte Carlo simulation

Reconstruct filtered piecewise constant functions with noise

Family of distributions

parametricFamily

Parametric families

Critical values

Confidence intervals for jumps and confidence bands for step functions

Piecewise constant multiscale inference

Jump estimation under restrictions

Robust standard deviation estimate

Piecewise constant regression with SMUCE

Automatic selection of number of jumps

Fitted step function

Solution path of step-functions

Forward selection of candidate jumps

Multiscale Change-Point Inference

TRANSIT algorithm for detecting jumps

testSmallScales

Test Small Scales

MRC.asymptotic.dyadic

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

Compute Multiresolution Criterion

Continuous time Markov chain

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

Compare fit blockwise with ground truth

Bounds based on MRC

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

Computation of the multiscale statistic

BesselPolynomial

Bessel Polynomials

Computation of the bounds