Package: march 3.3.2

march: Markov Chains

Computation of various Markovian models for categorical data including homogeneous Markov chains of any order, MTD models, Hidden Markov models, and Double Chain Markov Models.

Authors:Ogier Maitre and Kevin Emery, with contributions from Oliver Buschor and Andre Berchtold

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
march/json (API)

# Install 'march' in R:
install.packages('march', repos = c('https://andreberchtold.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.

1.15 score 1 stars 14 scripts 227 downloads 19 exports 0 dependencies

Last updated from:ccdf036b34. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK107
source / vignettesOK138
linux-releaseOK101
macos-releaseOK164
macos-oldrelOK182
windows-develOK75
windows-releaseOK57
windows-oldrelOK75
wasm-releaseOK73

Exports:march.AICmarch.BICmarch.dataset.h.extractSequencemarch.dataset.loadFromDataFramemarch.dataset.loadFromFilemarch.dcmm.constructmarch.dcmm.viterbimarch.indep.baileymarch.indep.constructmarch.indep.thompsonmarch.mc.baileymarch.mc.constructmarch.mc.thompsonmarch.mtd.baileymarch.mtd.constructmarch.mtd.thompsonmarch.readmarch.summarymarch.write

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Computation of Markovian models for categorical datamarch
Employment status in two categories (march dataset format)Employment.2
Compute Akaike Information Criterion (AIC). The AIC (Akaike Information Criterion) is computed for a given 'march.Model-class' according to the data used during construction.march.AIC
Compute Bayesian Information Criterion (BIC).march.BIC
Dataset for march package.march.Dataset-class
Extract a sequence from a dataset.march.dataset.h.extractSequence
Construct a dataset from a data.frame or a matrix.march.dataset.loadFromDataFrame
Load a dataset from a file.march.dataset.loadFromFile
A Double Chain Markov Model (DCMM).march.Dcmm-class
Construct a double chain Markov model (DCMM).march.dcmm.construct
Viterbi algorithm for a DCMM model.march.dcmm.viterbi
An independence model.march.Indep-class
Bailey Confidence Intervals for an Independence model.march.indep.bailey
Construct an independence model (zero-order Markov chain).march.indep.construct
Thompson Confidence Intervals for an Independence model.march.indep.thompson
A Markov chain of order >= 1.march.Mc-class
Bailey Confidence Intervals for a Markov chain.march.mc.bailey
Construct an homogeneous Markov Chain.march.mc.construct
Thompson Confidence Intervals for a Markov chain model.march.mc.thompson
A basic and virtual march model.march.Model-class
A Mixture Transition Distribution (MTD) model.march.Mtd-class
Bailey Confidence Intervals for a MTD model.march.mtd.bailey
Construct a Mixture Transition Distribution (MTD) model.march.mtd.construct
Thompson Confidence Intervals for a MTD model.march.mtd.thompson
Load a march.Model.march.read
march.Model Summary.march.summary
Save a march.Modelmarch.write
Song of the Wood Pewee (march dataset format)pewee
Song of the Wood Pewee (data frame format)pewee_df
Song of the Wood Pewee (text format)pewee_t
Sleep disorders (march dataset format)sleep
Sleep disorders (data frame format)sleep_df