Package: hdMTD 0.1.4
Maiara Gripp
hdMTD: Inference for High-Dimensional Mixture Transition Distribution Models
Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, 'hdMTD' includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) <http://jmlr.org/papers/v24/22-0266.html>.
Authors:
hdMTD_0.1.4.tar.gz
hdMTD_0.1.4.zip(r-4.7-any)hdMTD_0.1.4.zip(r-4.6-any)hdMTD_0.1.4.zip(r-4.5-any)
hdMTD_0.1.4.tgz(r-4.6-any)hdMTD_0.1.4.tgz(r-4.5-any)
hdMTD_0.1.4.tar.gz(r-4.7-any)hdMTD_0.1.4.tar.gz(r-4.6-any)
hdMTD_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hdMTD/json (API)
| # Install 'hdMTD' in R: |
| install.packages('hdMTD', repos = c('https://maiaragripp.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/maiaragripp/hdmtd/issues
- tempdata - Maximum temperatures in the city of Brasília, Brazil.
Last updated from:e238820abb. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 110 | ||
| source / vignettes | OK | 170 | ||
| linux-release | OK | 120 | ||
| macos-release | OK | 194 | ||
| macos-oldrel | OK | 196 | ||
| windows-devel | OK | 65 | ||
| windows-release | OK | 81 | ||
| windows-oldrel | OK | 92 | ||
| wasm-release | OK | 98 |
Exports:as.MTDcountsTabdTV_sampleempirical_probsfreqTabhdMTDhdMTD_BIChdMTD_CUThdMTD_FShdMTD_FSClagsLambdalambdasMTDestMTDmodeloscillationp0perfectSamplepjprobsSstatestransitP
Dependencies:clicpp11dplyrgenericsglueigraphlatticelifecyclemagrittrMatrixpillarpkgconfigpurrrR6rlangtibbletidyselectutf8vctrswithr
