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funtimes (version 10.0)

Functions for Time Series Analysis

Description

Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.

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Version

Install

install.packages('funtimes')

Monthly Downloads

827

Version

10.0

License

GPL (>= 2)

Maintainer

Vyacheslav Lyubchich

Last Published

December 5th, 2025

Functions in funtimes (10.0)

BICC

BIC-Based Spatio-Temporal Clustering
funtimes-defunct

Defunct functions in package funtimes.
funtimes-deprecated

Deprecated functions in package funtimes.
ccf_boot

Cross-Correlation of Autocorrelated Time Series
funtimes-package

funtimes: Functions for Time Series Analysis
mcusum_test

Change Point Test for Regression
notrend_test

Sieve Bootstrap Based Test for the Null Hypothesis of no Trend
sync_cluster

Time Series Clustering based on Trend Synchronism
cumsumCPA_test

Change Point Detection in Time Series via a Linear Regression with Temporally Correlated Errors
tails_i

Interval-Based Tails Comparison
sync_test

Time Series Trend Synchronicity Test
r_crit

Critical Value for Correlation Coefficient
tails_q

Quantile-Based Tails Comparison
purity

Clustering Purity
causality_pred

Out-of-sample Tests of Granger Causality
causality_predVAR

Out-of-sample Tests of Granger Causality using (Restricted) Vector Autoregression
wavk_test

WAVK Trend Test
ARest

Estimation of Autoregressive (AR) Parameters
AuePolyReg_test

Testing for Change Points in Time Series via Polynomial Regression
beales

Beale's Estimator and Sample Size
WAVK

WAVK Statistic
HVK

HVK Estimator
GombayCPA_test

Change Point Detection in Autoregressive Time Series
CSlideCluster

Slide-Level Time Series Clustering
DR

Downhill Riding (DR) Procedure
CWindowCluster

Window-Level Time Series Clustering