{ "_id": "6a105306acfb0bcc41ca1986", "Package": "markovchain", "Type": "Package", "Title": "Easy Handling Discrete Time Markov Chains", "Version": "0.10.3", "Authors@R": "c(\nperson(\"Giorgio Alfredo\", \"Spedicato\", role = c(\"aut\", \"cre\"),\nemail = \"spedicato_giorgio@yahoo.it\",comment = c(ORCID = \"0000-0002-0315-8888\")),\nperson(\"Tae\", \"Seung Kang\", role = \"aut\"),\nperson(\"Sai\", \"Bhargav Yalamanchi\", role = \"aut\"),\nperson(\"Mildenberger\", \"Thoralf\", role = \"ctb\", comment = c(ORCID = \"0000-0001-7242-1873\")),\nperson(\"Deepak\", \"Yadav\", role = \"aut\"),\nperson(\"Ignacio\", \"Cordón\", role = \"aut\", comment = c(ORCID = \"0000-0002-3152-0231\")),\nperson(\"Vandit\", \"Jain\", role = \"ctb\"),\nperson(\"Toni\", \"Giorgino\", role=\"ctb\", comment = c(ORCID = \"0000-0001-6449-0596\")),\nperson(\"Richèl J.C.\", \"Bilderbeek\", role = \"ctb\", comment = c(ORCID = \"0000-0003-1107-7049\")),\nperson(\"Daniel\", \"Ebbert\", email = \"daniel@ebbert.nrw\", role = \"ctb\", comment = c(ORCID = \"0000-0003-3666-7205\")),\nperson(\"Shreyash\", \"Maheshwari\", email = \"coolshreysh1000@gmail.com\", role = \"ctb\"),\nperson(\"Reinhold\", \"Koch\", role = \"ctb\")\n)", "Maintainer": "Giorgio Alfredo Spedicato ", "Description": "Functions and S4 methods to create and manage discrete\ntime Markov chains more easily. In addition functions to\nperform statistical (fitting and drawing random variates) and\nprobabilistic (analysis of their structural proprieties)\nanalysis are provided. See Spedicato (2017)\n. Some functions for continuous times\nMarkov chains depend on the suggested ctmcd package.", "License": "MIT + file LICENSE", "VignetteBuilder": "rmarkdown, knitr, bookdown, rticles", "SystemRequirements": "GNU make", "LazyLoad": "yes", "ByteCompile": "yes", "Encoding": "UTF-8", "BugReports": "https://github.com/spedygiorgio/markovchain/issues", "URL": "https://github.com/spedygiorgio/markovchain/", "RoxygenNote": "7.3.3", "Config/pak/sysreqs": "libglpk-dev make libxml2-dev", "Repository": "https://spedygiorgio.r-universe.dev", "Date/Publication": "2026-02-01 17:44:56 UTC", "RemoteUrl": "https://github.com/spedygiorgio/markovchain", "RemoteRef": "HEAD", "RemoteSha": "1ae4487e598982fd68a2ab3b292839eb40f73053", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-09 09:14:36 UTC", "User": "root" }, "Author": "Giorgio Alfredo Spedicato [aut, cre] (ORCID:\n),\nTae Seung Kang [aut],\nSai Bhargav Yalamanchi [aut],\nMildenberger Thoralf [ctb] (ORCID:\n),\nDeepak Yadav [aut],\nIgnacio Cordón [aut] (ORCID: ),\nVandit Jain [ctb],\nToni Giorgino [ctb] (ORCID: ),\nRichèl J.C. 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[ "Bottom", "2nd", "3rd", "Top" ], "rows": 4, "table": true, "tojson": false }, { "name": "craigsendi", "title": "CD4 cells counts on HIV Infects between zero and six month", "object": "craigsendi", "file": "craigsendi.rda", "class": [ "table" ], "fields": [ "0-49", "50-74", "75-UP" ], "rows": 3, "table": true, "tojson": false }, { "name": "holson", "title": "Holson data set", "object": "holson", "file": "holson.rda", "class": [ "data.frame" ], "fields": [ "id", "time1", "time2", "time3", "time4", "time5", "time6", "time7", "time8", "time9", "time10", "time11" ], "rows": 1000, "table": true, "tojson": true }, { "name": "kullback", "title": "Example from Kullback and Kupperman Tests for Contingency Tables", "object": "kullback", "file": "kullback.rda", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "preproglucacon", "title": "Preprogluccacon DNA protein bases sequences", "object": "preproglucacon", "file": "preproglucacon.rda", "class": [ "data.frame" ], "fields": [ "V1", "preproglucacon" ], "rows": 1572, "table": true, "tojson": true }, { "name": "rain", "title": "Alofi island daily rainfall", "object": "rain", "file": "rain.rda", "class": [ "data.frame" ], "fields": [ "V1", "rain" ], "rows": 1096, "table": true, "tojson": true }, { "name": "sales", "title": "Sales Demand Sequences", "object": "sales", "file": "sales.rda", "class": [ "matrix", "array" ], "fields": [ "A", "B", "C", "D", "E" ], "rows": 269, "table": true, "tojson": true }, { "name": "tm_abs", "title": "Single Year Corporate Credit Rating Transititions", "object": "tm_abs", "file": "tm_abs.rda", "class": [ "matrix", "array" ], "fields": [ "AAA", "AA", "A", "BBB", "BB", "B", "C", "D" ], "rows": 8, "table": true, "tojson": true } ], "_help": [ { "page": "markovchain-package", "title": "Easy Handling Discrete Time Markov Chains", "topics": [ "markovchain-package", "markovchain" ] }, { "page": "absorptionProbabilities", "title": "Absorption probabilities", "topics": [ "absorptionProbabilities" ] }, { "page": "blanden", "title": "Mobility between income quartiles", "topics": [ "blanden" ] }, { "page": "committorAB", "title": "Calculates committor of a markovchain object with respect to set A, B", "topics": [ "committorAB" ] }, { "page": "conditionalDistribution", "title": "'conditionalDistribution' of a Markov Chain", "topics": [ "conditionalDistribution" ] }, { "page": "craigsendi", "title": "CD4 cells counts on HIV Infects between zero and six month", "topics": [ "craigsendi" ] }, { "page": "markovchainFit", "title": "Function to fit a discrete Markov chain", "topics": [ "createSequenceMatrix", "markovchainFit" ] }, { "page": "ctmc-class", "title": "Continuous time Markov Chains class", "topics": [ "ctmc-class", "dim,ctmc-method", "initialize,ctmc_method", "plot,ctmc,missing-method", "states,ctmc-method", "steadyStates,ctmc-method" ] }, { "page": "ctmcFit", "title": "Function to fit a CTMC", "topics": [ "ctmcFit" ] }, { "page": "expectedRewards", "title": "Expected Rewards for a markovchain", "topics": [ "expectedRewards" ] }, { "page": "expectedRewardsBeforeHittingA", "title": "Expected first passage Rewards for a set of states in a markovchain", "topics": [ "expectedRewardsBeforeHittingA" ] }, { "page": "ExpectedTime", "title": "Returns expected hitting time from state i to state j", "topics": [ "ExpectedTime" ] }, { "page": "firstPassage", "title": "First passage across states", "topics": [ "firstPassage" ] }, { "page": "firstPassageMultiple", "title": "function to calculate first passage probabilities", "topics": [ "firstPassageMultiple" ] }, { "page": "fitHigherOrder", "title": "Functions to fit a higher order Markov chain", "topics": [ "fitHigherOrder", "seq2freqProb", "seq2matHigh" ] }, { "page": "fitHighOrderMultivarMC", "title": "Function to fit Higher Order Multivariate Markov chain", "topics": [ "fitHighOrderMultivarMC" ] }, { "page": "freq2Generator", "title": "Returns a generator matrix corresponding to frequency matrix", "topics": [ "freq2Generator" ] }, { "page": "generatorToTransitionMatrix", "title": "Function to obtain the transition matrix from the generator", "topics": [ "generatorToTransitionMatrix" ] }, { "page": "HigherOrderMarkovChain-class", "title": "Higher order Markov Chains class", "topics": [ "HigherOrderMarkovChain-class" ] }, { "page": "hittingProbabilities", "title": "Hitting probabilities for markovchain", "topics": [ "hittingProbabilities" ] }, { "page": "holson", "title": "Holson data set", "topics": [ "holson" ] }, { "page": "hommc-class", "title": "An S4 class for representing High Order Multivariate Markovchain (HOMMC)", "topics": [ "hommc", "hommc-class" ] }, { "page": "ictmc-class", "title": "An S4 class for representing Imprecise Continuous Time Markovchains", "topics": [ "ictmc", "ictmc-class" ] }, { "page": "impreciseProbabilityatT", "title": "Calculating full conditional probability using lower rate transition matrix", "topics": [ "impreciseProbabilityatT" ] }, { "page": "inferHyperparam", "title": "Function to infer the hyperparameters for Bayesian inference from an a priori matrix or a data set", "topics": [ "inferHyperparam" ] }, { "page": "is.accessible", "title": "Verify if a state j is reachable from state i.", "topics": [ "is.accessible" ] }, { "page": "is.CTMCirreducible", "title": "Check if CTMC is irreducible", "topics": [ "is.CTMCirreducible" ] }, { "page": "is.irreducible", "title": "Function to check if a Markov chain is irreducible (i.e. ergodic)", "topics": [ "is.irreducible" ] }, { "page": "is.regular", "title": "Check if a DTMC is regular", "topics": [ "is.regular" ] }, { "page": "is.TimeReversible", "title": "checks if ctmc object is time reversible", "topics": [ "is.TimeReversible" ] }, { "page": "kullback", "title": "Example from Kullback and Kupperman Tests for Contingency Tables", "topics": [ "kullback" ] }, { "page": "markovchain-class", "title": "Markov Chain class", "topics": [ "!=,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,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" ] }, { "page": "markovchainList-class", "title": "Non homogeneus discrete time Markov Chains class", "topics": [ "dim,markovchainList-method", "markovchainList-class", "predict,markovchainList-method", "print,markovchainList-method", "show,markovchainList-method", "[[,markovchainList-method" ] }, { "page": "markovchainListFit", "title": "markovchainListFit", "topics": [ "markovchainListFit" ] }, { "page": "markovchainSequence", "title": "Function to generate a sequence of states from homogeneous Markov chains.", "topics": [ "markovchainSequence" ] }, { "page": "meanAbsorptionTime", "title": "Mean absorption time", "topics": [ "meanAbsorptionTime" ] }, { "page": "meanFirstPassageTime", "title": "Mean First Passage Time for irreducible Markov chains", "topics": [ "meanFirstPassageTime" ] }, { "page": "meanNumVisits", "title": "Mean num of visits for markovchain, starting at each state", "topics": [ "meanNumVisits" ] }, { "page": "meanRecurrenceTime", "title": "Mean recurrence time", "topics": [ "meanRecurrenceTime" ] }, { "page": "multinomialConfidenceIntervals", "title": "A function to compute multinomial confidence intervals of DTMC", "topics": [ "multinomialConfidenceIntervals" ] }, { "page": "getName", "title": "Method to retrieve name of markovchain object", "topics": [ "name", "name,markovchain-method" ] }, { "page": "setName", "title": "Method to set name of markovchain object", "topics": [ "name<-", "name<-,markovchain-method" ] }, { "page": "names", "title": "Returns the states for a Markov chain object", "topics": [ "names,markovchain-method" ] }, { "page": "noofVisitsDist", "title": "return a joint pdf of the number of visits to the various states of the DTMC", "topics": [ "noofVisitsDist" ] }, { "page": "ones", "title": "Returns an Identity matrix", "topics": [ "ones" ] }, { "page": "structuralAnalysis", "title": "Various function to perform structural analysis of DTMC", "topics": [ "absorbingStates", "canonicForm", "communicatingClasses", "period", "recurrentClasses", "recurrentStates", "transientClasses", "transientStates" ] }, { "page": "predictHommc", "title": "Simulate a higher order multivariate markovchain", "topics": [ "predictHommc" ] }, { "page": "predictiveDistribution", "title": "predictiveDistribution", "topics": [ "predictiveDistribution" ] }, { "page": "preproglucacon", "title": "Preprogluccacon DNA protein bases sequences", "topics": [ "preproglucacon" ] }, { "page": "priorDistribution", "title": "priorDistribution", "topics": [ "priorDistribution" ] }, { "page": "probabilityatT", "title": "Calculating probability from a ctmc object", "topics": [ "probabilityatT" ] }, { "page": "rain", "title": "Alofi island daily rainfall", "topics": [ "rain" ] }, { "page": "rctmc", "title": "rctmc", "topics": [ "rctmc" ] }, { "page": "rmarkovchain", "title": "Function to generate a sequence of states from homogeneous or non-homogeneous Markov chains.", "topics": [ "rmarkovchain" ] }, { "page": "sales", "title": "Sales Demand Sequences", "topics": [ "sales" ] }, { "page": "hommc-show", "title": "Function to display the details of hommc object", "topics": [ "show,hommc-method" ] }, { "page": "states", "title": "Defined states of a transition matrix", "topics": [ "states", "states,markovchain-method" ] }, { "page": "steadyStates", "title": "Stationary states of a 'markovchain' object", "topics": [ "steadyStates" ] }, { "page": "tm_abs", "title": "Single Year Corporate Credit Rating Transititions", "topics": [ "tm_abs" ] }, { "page": "transition2Generator", "title": "Return the generator matrix for a corresponding transition matrix", "topics": [ "transition2Generator" ] }, { "page": "transitionProbability", "title": "Function to get the transition probabilities from initial to subsequent states.", "topics": [ "transitionProbability", "transitionProbability,markovchain-method" ] }, { "page": "statisticalTests", "title": "Various functions to perform statistical inference of DTMC", "concept": [ "statisticalTests" ], "topics": [ "assessOrder", "assessStationarity", "verifyEmpiricalToTheoretical", "verifyHomogeneity", "verifyMarkovProperty" ] }, { "page": "zeros", "title": "Matrix to create zeros", "topics": [ "zeros" ] } ], "_readme": "https://github.com/spedygiorgio/markovchain/raw/HEAD/README.md", "_rundeps": [ "cli", "cpp11", "expm", "glue", "igraph", "lattice", "lifecycle", "magrittr", "Matrix", "pkgconfig", "Rcpp", "RcppArmadillo", "RcppParallel", "rlang", "vctrs" ], "_sysdeps": [ { "shlib": "liblapack", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.26+ds-1ubuntu0.1", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libblas", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.26+ds-1ubuntu0.1", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "an_introduction_to_markovchain_package.Rmd", "filename": "an_introduction_to_markovchain_package.html", "title": "The markovchain Package: A Package for Easily Handling Discrete Markov Chains in R", "author": "Giorgio Alfredo Spedicato, Tae Seung Kang, Sai Bhargav Yalamanchi, Deepak Yadav, Ignacio Cordón", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Review of core mathematical concepts", "General Definitions", "Properties and classification of states", "A short example", "The structure of the package", "Creating markovchain objects", "Handling markovchain objects", "Probability with markovchain objects", "Conditional distributions", "Stationary states", "Classification of states", "First passage time distributions and means", "Mean recurrence time", "Absorption probabilities and mean absorption time", "Committor probability", "Hitting probabilities", "Statistical analysis", "Simulation", "Estimation", "Prediction", "Predicting from a markovchain object", "Predicting from a markovchainList object", "Statistical Tests", "Assessing the Markov property of a Markov chain sequence", "Assessing the order of a Markov chain sequence", "Assessing the stationarity of a Markov chain sequence", "Divergence tests for empirically estimated transition matrices", "Continuous Times Markov Chains", "Intro", "Stationary Distributions", "Expected Hitting Time", "Probability at time t", "Examples", "Pseudo - Bayesian Estimation", "Bayesian Estimation", "Notation and set-up", "Methods", "Predictive distribution", "Choosing the hyper-parameters", "Usage and examples", "Applications", "Weather forecasting", "Land of Oz", "Alofi Island Rainfall", "Finance and Economics", "Finance", "Economics", "Actuarial science", "MPTL Bonus Malus", "Health insurance example", "Sociology", "Genetics and Medicine", "Genetics", "Medicine", "Discussion, issues and future plans", "Acknowledgments", "References" ], "created": "2018-12-31 14:31:34", "modified": "2024-10-07 20:21:50", "commits": 42 }, { "source": "gsoc_2017_additions.Rmd", "filename": "gsoc_2017_additions.html", "title": "Google Summer of Code 2017 Additions", "author": "Vandit Jain", "engine": "knitr::rmarkdown", "headings": [ "Expected Hitting Time using CTMC", "Calculating Probability at time T using ctmc", "Plotting generator matrix of continuous-time markovchains", "Imprecise Continuous-Time Markov chains", "Types of ICTMCs", "Lower Transition Rate Operators for ICTMCs", "Lower Transition Operators", "ImpreciseprobabilityatT function", "Continuous time markovchain generator using frequency Matrix", "Committor of a markovchain", "First Passage probability for set of states", "Joint PDF of number of visits to the various states of a markovchain", "Expected Rewards for a markovchain", "Expected Rewards for a set of states in a markovchain process", "Checking Irreducibly of a CTMC", "Simulation of Higher Order Multivariate Markovchains", "Check Time Reversibility of Continuous-time markovchains", "References" ], "created": "2019-07-21 11:44:18", "modified": "2024-02-25 11:17:35", "commits": 9 }, { "source": "higher_order_markov_chains.Rmd", "filename": "higher_order_markov_chains.html", "title": "Higher order Markov chains", "author": "Deepak Yadav, Tae Seung Kang, Giorgio Alfredo Spedicato", "engine": "knitr::rmarkdown", "headings": [ "Higher Order Markov Chains", "Higher Order Multivariate Markov Chains", "Introduction", "Representation of parameters in the code", "Definition of HOMMC class", "How to create an object of class HOMMC", "Fit HOMMC", "A Marketing Example", "References" ], "created": "2018-12-31 14:31:34", "modified": "2024-02-25 11:24:44", "commits": 17 } ], "_score": 12.950715664997803, "_indexed": true, "_nocasepkg": "markovchain", "_universes": [ "spedygiorgio" ], "_binaries": [ { "r": "4.7.0", "os": 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