Package: Copula.Markov 2.9

Copula.Markov: Copula-Based Estimation and Statistical Process Control for Serially Correlated Time Series

Estimation and statistical process control are performed under copula-based time-series models. Available are statistical methods in Long and Emura (2014 JCSA), Emura et al. (2017 Commun Stat-Simul) <doi:10.1080/03610918.2015.1073303>, Huang and Emura (2021 Commun Stat-Simul) <doi:10.1080/03610918.2019.1602647>, Lin et al. (2021 Comm Stat-Simul) <doi:10.1080/03610918.2019.1652318>, Sun et al. (2020 JSS Series in Statistics)<doi:10.1007/978-981-15-4998-4>, and Huang and Emura (2021, in revision).

Authors:Takeshi Emura, Xinwei Huang, Ting-Hsuan Long, Li-Hsien Sun

Copula.Markov_2.9.tar.gz
Copula.Markov_2.9.zip(r-4.7)Copula.Markov_2.9.zip(r-4.6)Copula.Markov_2.9.zip(r-4.5)
Copula.Markov_2.9.tgz(r-4.6-any)Copula.Markov_2.9.tgz(r-4.5-any)
Copula.Markov_2.9.tar.gz(r-4.7-any)Copula.Markov_2.9.tar.gz(r-4.6-any)
Copula.Markov_2.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
Copula.Markov/json (API)

# Install 'Copula.Markov' in R:
install.packages('Copula.Markov', repos = c('https://takeshiemura1.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 3 stars 9 scripts 219 downloads 14 exports 0 dependencies

Last updated from:f27c0753c1. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK115
source / vignettesOK145
linux-release-x86_64OK108
macos-release-arm64OK107
macos-oldrel-arm64OK74
windows-develOK77
windows-releaseOK71
windows-oldrelOK85
wasm-releaseOK80

Exports:Clayton.Markov.DATAClayton.Markov.DATA.binomClayton.Markov.GOFClayton.Markov.GOF.binomClayton.Markov.MLEClayton.Markov.MLE.binomClayton.Markov2.DATAClayton.Markov2.MLEClayton.MixNormal.Markov.MLEJoe.Markov.DATAJoe.Markov.DATA.binomJoe.Markov.GOF.binomJoe.Markov.MLEJoe.Markov.MLE.binom

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Copula-Based Estimation and Statistical Process Control for Serially Correlated Time SeriesCopula.Markov-package Copula.Markov
Generating Time Series Data Under a Copula-Based Markov Chain Model with the Clayton CopulaClayton.Markov.DATA
Generating Time Series Data Under a Copula-Based Markov Chain Model with the Clayton Copula and Binomial Margin.Clayton.Markov.DATA.binom
A goodness-of-fit test for the marginal normal distribution.Clayton.Markov.GOF
A goodness-of-fit test for the marginal binomial distribution.Clayton.Markov.GOF.binom
Maximum Likelihood Estimation and Statistical Process Control Under the Clayton CopulaClayton.Markov.MLE
Maximum Likelihood Estimation and Statistical Process Control Under the Clayton CopulaClayton.Markov.MLE.binom
Generating Time Series Data Under a Copula-Based 2nd-order Markov Chain Model with the Clayton CopulaClayton.Markov2.DATA
Maximum Likelihood Estimation and Statistical Process Control Under the Clayton Copula with a 2nd order Markov chain.Clayton.Markov2.MLE
Maximum Likelihood Estimation using Newton-Raphson Method Under the Clayton Copula and the Mix-Normal distributionClayton.MixNormal.Markov.MLE
Dow Jones Industrial AverageDowJones
Generating Time Series Data Under a Copula-Based Markov Chain Model with the Joe CopulaJoe.Markov.DATA
Generating Time Series Data Under a Copula-Based Markov Chain Model with the Joe Copula and Binomial Margin.Joe.Markov.DATA.binom
A goodness-of-fit test for the marginal binomial distribution.Joe.Markov.GOF.binom
Maximum Likelihood Estimation and Statistical Process Control Under the Joe CopulaJoe.Markov.MLE
Maximum Likelihood Estimation and Statistical Process Control Under the Joe CopulaJoe.Markov.MLE.binom