Package: markovchain 0.9.6
Giorgio Alfredo Spedicato
markovchain: Easy Handling Discrete Time Markov Chains
Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depends on the suggested ctmcd package, that, as May 2023, can be retrieved from <https://cran.r-project.org/src/contrib/Archive/ctmcd/ctmcd_1.4.2.tar.gz>.
Authors:
markovchain_0.9.6.tar.gz
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markovchain.pdf |markovchain.html✨
markovchain/json (API)
NEWS
| # Install 'markovchain' in R: |
| install.packages('markovchain', repos = c('https://spedygiorgio.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/spedygiorgio/markovchain/issues
- blanden - Mobility between income quartiles
- craigsendi - CD4 cells counts on HIV Infects between zero and six month
- holson - Holson data set
- kullback - Example from Kullback and Kupperman Tests for Contingency Tables
- preproglucacon - Preprogluccacon DNA protein bases sequences
- rain - Alofi island daily rainfall
- sales - Sales Demand Sequences
- tm_abs - Single Year Corporate Credit Rating Transititions
ctmcdtmcmarkov-chainmarkov-modelr-programmingrcpp
Last updated 22 days agofrom:6d6dfd4e80. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 27 2024 |
| R-4.5-win-x86_64 | NOTE | Aug 27 2024 |
| R-4.5-linux-x86_64 | NOTE | Aug 27 2024 |
| R-4.4-win-x86_64 | NOTE | Aug 27 2024 |
| R-4.4-mac-x86_64 | NOTE | Aug 27 2024 |
| R-4.4-mac-aarch64 | NOTE | Aug 27 2024 |
| R-4.3-win-x86_64 | NOTE | Aug 27 2024 |
| R-4.3-mac-x86_64 | NOTE | Aug 27 2024 |
| R-4.3-mac-aarch64 | NOTE | Aug 27 2024 |
Exports:absorbingStatesabsorptionProbabilitiesassessOrderassessStationaritycanonicFormcoercecommittorABcommunicatingClassesconditionalDistributioncreateSequenceMatrixctmcFitexpectedRewardsexpectedRewardsBeforeHittingAExpectedTimefirstPassagefirstPassageMultiplefitHigherOrderfitHighOrderMultivarMCfreq2GeneratorgeneratorToTransitionMatrixhittingProbabilitiesimpreciseProbabilityatTinferHyperparamis.accessibleis.CTMCirreducibleis.irreducibleis.regularis.TimeReversiblemarkovchainFitmarkovchainListFitmarkovchainSequencemeanAbsorptionTimemeanFirstPassageTimemeanNumVisitsmeanRecurrenceTimemultinomialConfidenceIntervalsnamename<-noofVisitsDistperiodplotpredictpredictHommcpredictiveDistributionprintpriorDistributionprobabilityatTrctmcrecurrentClassesrecurrentStatesrmarkovchainseq2freqProbseq2matHighshowstatessteadyStatessummaryttransientClassestransientStatestransition2GeneratortransitionProbabilityverifyEmpiricalToTheoreticalverifyHomogeneityverifyMarkovProperty
Dependencies:clicpp11expmglueigraphlatticelifecyclemagrittrMatrixpkgconfigRcppRcppArmadilloRcppParallelrlangvctrs
The markovchain Package: A Package for Easily Handling Discrete Markov Chains in R
Rendered froman_introduction_to_markovchain_package.Rmdusingknitr::rmarkdownon Aug 27 2024.Last update: 2024-01-01
Started: 2018-12-31
Google Summer of Code 2017 Additions
Rendered fromgsoc_2017_additions.Rmdusingknitr::rmarkdownon Aug 27 2024.Last update: 2024-02-25
Started: 2019-07-21
Higher order Markov chains
Rendered fromhigher_order_markov_chains.Rmdusingknitr::rmarkdownon Aug 27 2024.Last update: 2024-02-25
Started: 2018-12-31
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Easy Handling Discrete Time Markov Chains | markovchain-package markovchain |
| Absorption probabilities | absorptionProbabilities |
| Mobility between income quartiles | blanden |
| Calculates committor of a markovchain object with respect to set A, B | committorAB |
| 'conditionalDistribution' of a Markov Chain | conditionalDistribution |
| CD4 cells counts on HIV Infects between zero and six month | craigsendi |
| Function to fit a discrete Markov chain | createSequenceMatrix markovchainFit |
| Continuous time Markov Chains class | ctmc-class dim,ctmc-method initialize,ctmc_method plot,ctmc,missing-method states,ctmc-method steadyStates,ctmc-method |
| Function to fit a CTMC | ctmcFit |
| Expected Rewards for a markovchain | expectedRewards |
| Expected first passage Rewards for a set of states in a markovchain | expectedRewardsBeforeHittingA |
| Returns expected hitting time from state i to state j | ExpectedTime |
| First passage across states | firstPassage |
| function to calculate first passage probabilities | firstPassageMultiple |
| Functions to fit a higher order Markov chain | fitHigherOrder seq2freqProb seq2matHigh |
| Function to fit Higher Order Multivariate Markov chain | fitHighOrderMultivarMC |
| Returns a generator matrix corresponding to frequency matrix | freq2Generator |
| Function to obtain the transition matrix from the generator | generatorToTransitionMatrix |
| Higher order Markov Chains class | HigherOrderMarkovChain-class |
| Hitting probabilities for markovchain | hittingProbabilities |
| Holson data set | holson |
| An S4 class for representing High Order Multivariate Markovchain (HOMMC) | hommc hommc-class |
| An S4 class for representing Imprecise Continuous Time Markovchains | ictmc ictmc-class |
| Calculating full conditional probability using lower rate transition matrix | impreciseProbabilityatT |
| Function to infer the hyperparameters for Bayesian inference from an a priori matrix or a data set | inferHyperparam |
| Verify if a state j is reachable from state i. | is.accessible |
| Check if CTMC is irreducible | is.CTMCirreducible |
| Function to check if a Markov chain is irreducible (i.e. ergodic) | is.irreducible |
| Check if a DTMC is regular | is.regular |
| checks if ctmc object is time reversible | is.TimeReversible |
| Example from Kullback and Kupperman Tests for Contingency Tables | kullback |
| Markov Chain class | !=,markovchain,markovchain-method *,markovchain,markovchain-method *,markovchain,matrix-method *,markovchain,numeric-method *,matrix,markovchain-method *,numeric,markovchain-method ==,markovchain,markovchain-method absorbingStates,markovchain-method absorptionProbabilities,markovchain-method canonicForm,markovchain-method coerce,data.frame,markovchain-method coerce,etm,markovchain-method coerce,markovchain,data.frame-method coerce,markovchain,igraph-method coerce,markovchain,matrix-method coerce,markovchain,sparseMatrix-method coerce,matrix,markovchain-method coerce,msm,markovchain-method coerce,msm.est,markovchain-method coerce,sparseMatrix,markovchain-method coerce,table,markovchain-method communicatingClasses,markovchain-method conditionalDistribution,markovchain-method dim,markovchain-method hittingProbabilities,markovchain-method initialize,markovchain-method is.accessible,markovchain,character,character-method is.accessible,markovchain,missing,missing-method is.irreducible,markovchain-method is.regular,markovchain-method markovchain-class meanAbsorptionTime,markovchain-method meanFirstPassageTime,markovchain,character-method meanFirstPassageTime,markovchain,missing-method meanNumVisits,markovchain-method meanRecurrenceTime,markovchain-method names<-,markovchain-method plot,markovchain,missing-method predict,markovchain-method print,markovchain-method recurrentClasses,markovchain-method recurrentStates,markovchain-method show,markovchain-method sort,markovchain-method steadyStates,markovchain-method summary,markovchain-method t,markovchain-method transientClasses,markovchain-method transientStates,markovchain-method [,markovchain,ANY,ANY,ANY-method ^,markovchain,numeric-method |
| Non homogeneus discrete time Markov Chains class | dim,markovchainList-method markovchainList-class predict,markovchainList-method print,markovchainList-method show,markovchainList-method [[,markovchainList-method |
| markovchainListFit | markovchainListFit |
| Function to generate a sequence of states from homogeneous Markov chains. | markovchainSequence |
| Mean absorption time | meanAbsorptionTime |
| Mean First Passage Time for irreducible Markov chains | meanFirstPassageTime |
| Mean num of visits for markovchain, starting at each state | meanNumVisits |
| Mean recurrence time | meanRecurrenceTime |
| A function to compute multinomial confidence intervals of DTMC | multinomialConfidenceIntervals |
| Method to retrieve name of markovchain object | name name,markovchain-method |
| Method to set name of markovchain object | name<- name<-,markovchain-method |
| Returns the states for a Markov chain object | names,markovchain-method |
| return a joint pdf of the number of visits to the various states of the DTMC | noofVisitsDist |
| Returns an Identity matrix | ones |
| Various function to perform structural analysis of DTMC | absorbingStates canonicForm communicatingClasses period recurrentClasses recurrentStates transientClasses transientStates |
| Simulate a higher order multivariate markovchain | predictHommc |
| predictiveDistribution | predictiveDistribution |
| Preprogluccacon DNA protein bases sequences | preproglucacon |
| priorDistribution | priorDistribution |
| Calculating probability from a ctmc object | probabilityatT |
| Alofi island daily rainfall | rain |
| rctmc | rctmc |
| Function to generate a sequence of states from homogeneous or non-homogeneous Markov chains. | rmarkovchain |
| Sales Demand Sequences | sales |
| Function to display the details of hommc object | show,hommc-method |
| Defined states of a transition matrix | states states,markovchain-method |
| Stationary states of a 'markovchain' object | steadyStates |
| Single Year Corporate Credit Rating Transititions | tm_abs |
| Return the generator matrix for a corresponding transition matrix | transition2Generator |
| Function to get the transition probabilities from initial to subsequent states. | transitionProbability transitionProbability,markovchain-method |
| Various functions to perform statistical inference of DTMC | assessOrder assessStationarity verifyEmpiricalToTheoretical verifyHomogeneity verifyMarkovProperty |
| Matrix to create zeros | zeros |