{ "_id": "6aca48adfe5cc25786e8693d", "Package": "markovchain", "Type": "Package", "Title": "Easy Handling Discrete Time Markov Chains", "Version": "1.2", "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": "Description: Functions and S4 classes to create, manage\nand analyse discrete time Markov chains. Includes probabilistic\nanalysis of chain structure (state classification, hitting\ntimes, stationary distributions, spectral and mixing\ndiagnostics), statistical inference (maximum likelihood and\nBayesian estimation, tests of the Markov property, homogeneity,\nstationarity and order, simulation), higher-order and\nmultivariate chains, state aggregation, and import/export to\nJSON, YAML and CSV. Basic support for continuous time Markov\nchains is also provided; some of these functions depend on the\nsuggested 'ctmcd' package. See Spedicato (2017)\n.", "License": "MIT + file LICENSE", "VignetteBuilder": "rmarkdown, knitr, bookdown, rticles, quarto", "SystemRequirements": "GNU make, Quarto", "LazyLoad": "yes", "ByteCompile": "yes", "Encoding": "UTF-8", "BugReports": "https://github.com/spedygiorgio/markovchain/issues", "URL": "https://github.com/spedygiorgio/markovchain/", "NeedsCompilation": "yes", "Config/roxygen2/version": "8.1.0", "Config/pak/sysreqs": "cmake libglpk-dev make libxml2-dev", "Repository": "https://spedygiorgio.r-universe.dev", "Date/Publication": "2026-10-10 07:43:05 UTC", "RemoteUrl": "https://github.com/spedygiorgio/markovchain", "RemoteRef": "HEAD", "RemoteSha": "a796eb9486027099ca7a2b648b39623f7d4f0a87", "Packaged": { "Date": "2026-10-10 13:58:53 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. Bilderbeek [ctb] (ORCID:\n),\nDaniel Ebbert [ctb] (ORCID: ),\nShreyash Maheshwari [ctb],\nReinhold Koch [ctb]", "_user": "spedygiorgio", "_type": "src", "_file": "markovchain_1.2.tar.gz", "_fileid": "https://r2.ropensci.org/0e46d8876714be2d2c680923d8a4868cd4d0e929f17476b801197a645673ccbf", "_filesize": 1844393, "_sha256": "0e46d8876714be2d2c680923d8a4868cd4d0e929f17476b801197a645673ccbf", "_expires": "2027-01-18T14:16:10.000Z", "_created": "2026-10-10T13:58:53.000Z", "_published": "2026-10-10T14:16:13.186Z", "_buildurl": "https://github.com/r-universe/spedygiorgio/actions/runs/38057510217", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 114230075490, "time": 218, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "11671564954" }, { "job": 114230075512, "time": 273, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11671929733" }, { "job": 114230075475, "time": 257, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11672069676" }, { "job": 114230075470, "time": 256, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11671443457" }, { "job": 114230075535, "time": 279, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11671049400" }, { "job": 114230075524, "time": 353, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11672221248" }, { "job": 114230075545, "time": 255, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11671034269" }, { "job": 114230075529, "time": 630, "config": "macos-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11672521066" }, { "job": 114228956258, "time": 365, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11671829229" }, { "job": 114230075436, "time": 127, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11672034607" }, { "job": 114230075560, "time": 473, "config": "windows-devel-arm64", "r": "4.7.0", "check": "ERROR", "artifact": "11672335573" }, { "job": 114230075557, "time": 295, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11672485072" }, { "job": 114230075580, "time": 283, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11671919909" }, { "job": 114230075616, "time": 451, "config": "windows-release-arm64", "r": "4.6.1", "check": "ERROR", "artifact": "11672044989" }, { "job": 114230075520, "time": 294, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11671333600" } ], "_upstream": "https://github.com/spedygiorgio/markovchain", "_commit": { "id": "a796eb9486027099ca7a2b648b39623f7d4f0a87", "author": "Giorgio Alfredo Spedicato ", "committer": "GitHub ", "message": "fix(serialization): .asDoubleExact() exact for small and large probabilities too (#292)\n\nThe helper added in #291 only handled decimal exponents up to 22 in\nabsolute value and fell back to as.numeric() beyond that. With 17\nsignificant digits that excluded every value below about 1e-5, for which\nas.numeric() is exact on x86 but not on arm64 macOS, so the unit test\n.asDoubleExact(sprintf('%.17g', x)) still failed on macos-latest.\n\nThe scale is now applied in several exact steps (1e22, then 10^r), up to\na decimal exponent of 280 in absolute value, keeping the integer as a\ndouble-double throughout; a non-finite result keeps the as.numeric()\nvalue. The test no longer compares with R literals of 16 digits (also\nparsed inexactly on arm64) and runs the helper with an emulated\ndouble-only as.numeric(), so it checks the arm64 behaviour on any\nplatform.\n\n\nClaude-Session: https://claude.ai/code/session_013Gr4kkfqPmDkkGZuKtyUqn\n\nCo-authored-by: Claude ", "time": 1791618185 }, "_maintainer": { "name": "Giorgio Alfredo Spedicato", "email": "spedicato_giorgio@yahoo.it", "orcid": "0000-0002-0315-8888" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.4.0", "role": "Depends" }, { "package": "Matrix", "version": ">= 1.5-0", "role": "Depends" }, { "package": "methods", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppParallel", "role": "LinkingTo" }, { "package": "RcppArmadillo", "version": ">= 0.9.600.4.0", "role": "LinkingTo" }, { "package": "igraph", "version": ">= 1.0.0", "role": "Imports" }, { "package": "expm", "role": "Imports" }, { "package": 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"markovchain-package", "title": "Easy Handling Discrete Time Markov Chains", "topics": [ "markovchain-package", "markovchain" ] }, { "page": "absorptionProbabilities", "title": "Absorption probabilities", "topics": [ "absorptionProbabilities" ] }, { "page": "aggregateStates", "title": "Aggregate a Markov chain's state space by Kullback-Leibler minimization", "topics": [ "aggregateStates", "aggregateStates,markovchain-method" ] }, { "page": "assessIndependence", "title": "Test independence of consecutive states of an empirical sequence", "concept": [ "statisticalTests" ], "topics": [ "assessIndependence" ] }, { "page": "autoLump", "title": "Automatically aggregate a Markov chain by spectral clustering", "topics": [ "autoLump", "autoLump,markovchain-method" ] }, { "page": "autoplot.markovchain", "title": "Plot a Markov chain with ggplot2", "topics": [ "autoplot.markovchain" ] }, { "page": "birthDeath", "title": "Build a birth-death Markov chain", "topics": [ "birthDeath" ] }, { "page": "blanden", "title": "Mobility between income quartiles", "topics": [ "blanden" ] }, { "page": "closestReversible", "title": "Closest reversible approximation of a Markov chain", "topics": [ "closestReversible", "closestReversible,markovchain-method" ] }, { "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": "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": "dirichletChain", "title": "Markov chain from a Dirichlet process", "topics": [ "dirichletChain" ] }, { "page": "entropyRate", "title": "Entropy rate of a Markov chain", "topics": [ "entropyRate", "entropyRate,markovchain-method" ] }, { "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": "fitMTD", "title": "Fit a mixture transition distribution (MTD) model", "topics": [ "fitMTD" ] }, { "page": "freq2Generator", "title": "Returns a generator matrix corresponding to frequency matrix", "topics": [ "freq2Generator" ] }, { "page": "fundamentalMatrix", "title": "Fundamental matrix of an absorbing Markov chain", "topics": [ "fundamentalMatrix" ] }, { "page": "gamblersRuin", "title": "Build a gambler's ruin Markov chain", "topics": [ "gamblersRuin" ] }, { "page": "generatorToTransitionMatrix", "title": "Function to obtain the transition matrix from the generator", "topics": [ "generatorToTransitionMatrix" ] }, { "page": "higherOrderLogLik", "title": "Log-likelihood, deviance and information criteria of a higher order Markov chain", "topics": [ "higherOrderLogLik" ] }, { "page": "HigherOrderMarkovChain-class", "title": "Higher order Markov Chains class", "topics": [ "HigherOrderMarkovChain-class" ] }, { "page": "higherOrderPredict", "title": "Next-state probabilities and simulation for higher order Markov chains", "topics": [ "higherOrderPredict", "higherOrderSimulate" ] }, { "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": "identityChain", "title": "Identity Markov chain", "topics": [ "identityChain" ] }, { "page": "impliedTimescales", "title": "Implied timescales of a Markov chain", "topics": [ "impliedTimescales", "impliedTimescales,markovchain-method" ] }, { "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.lumpable", "title": "Check exact lumpability of a Markov chain", "topics": [ "is.lumpable", "is.lumpable,markovchain-method" ] }, { "page": "is.regular", "title": "Check if a DTMC is regular", "topics": [ "is.regular" ] }, { "page": "is.reversible", "title": "Check whether a Markov chain is reversible", "topics": [ "is.reversible", "is.reversible,markovchain-method" ] }, { "page": "is.stochasticallyMonotone", "title": "Check if a Markov chain is stochastically monotone", "topics": [ "is.stochasticallyMonotone", "is.stochasticallyMonotone,ANY-method", "is.stochasticallyMonotone,markovchain-method", "is.stochasticallyMonotone,matrix-method" ] }, { "page": "is.TimeReversible", "title": "checks if ctmc object is time reversible", "topics": [ "is.TimeReversible" ] }, { "page": "kemenyConstant", "title": "Kemeny's constant of a Markov chain", "topics": [ "kemenyConstant", "kemenyConstant,markovchain-method" ] }, { "page": "kullback", "title": "Example from Kullback and Kupperman Tests for Contingency Tables", "topics": [ "kullback" ] }, { "page": "lazyChain", "title": "Build a lazy version of a Markov chain", "topics": [ "lazyChain", "lazyChain,markovchain-method" ] }, { "page": "lump", "title": "Aggregate a Markov chain over a partition", "topics": [ "lump", "lump,markovchain-method" ] }, { "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": "markovchainFit", "title": "Function to fit a discrete Markov chain", "topics": [ ".markovchainFitRcpp", "createSequenceMatrix", "markovchainFit" ] }, { "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": "mergeWith", "title": "Merge two Markov chains by convex combination of their transition matrices", "topics": [ "mergeWith", "mergeWith,markovchain,markovchain-method" ] }, { "page": "mixingTime", "title": "Mixing time of a Markov chain", "topics": [ "mixingTime", "mixingTime,markovchain-method" ] }, { "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": "Expected fraction of the first N steps spent in each state", "topics": [ "noofVisitsDist" ] }, { "page": "normalizedEntropyRate", "title": "Normalized entropy rate of a Markov chain", "topics": [ "normalizedEntropyRate", "normalizedEntropyRate,markovchain-method" ] }, { "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": "populationGeneticsModel", "title": "Build a population-genetics Markov chain (Moran or Wright-Fisher)", "topics": [ "populationGeneticsModel" ] }, { "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": "randomMarkovChain", "title": "Random Markov chain", "topics": [ "randomMarkovChain" ] }, { "page": "rctmc", "title": "rctmc", "topics": [ "rctmc" ] }, { "page": "redistribute", "title": "Evolution of a distribution over time", "topics": [ "redistribute", "redistribute,markovchain-method" ] }, { "page": "relaxationTime", "title": "Relaxation time of a Markov chain", "topics": [ "relaxationTime", "relaxationTime,markovchain-method" ] }, { "page": "rmarkovchain", "title": "Function to generate a sequence of states from homogeneous or non-homogeneous Markov chains.", "topics": [ "rmarkovchain" ] }, { "page": "rouwenhorst", "title": "Discretize an AR(1) process into a Markov chain (Rouwenhorst's method)", "topics": [ "rouwenhorst" ] }, { "page": "sales", "title": "Sales Demand Sequences", "topics": [ "sales" ] }, { "page": "selectOrder", "title": "Select the order of a Markov chain by information criteria", "topics": [ "selectOrder" ] }, { "page": "sensitivity", "title": "Sensitivity of the stationary distribution to a state's transition row", "topics": [ "sensitivity", "sensitivity,markovchain-method" ] }, { "page": "hommc-show", "title": "Function to display the details of hommc object", "topics": [ "show,hommc-method" ] }, { "page": "slem", "title": "Second largest eigenvalue modulus (SLEM) of a Markov chain", "topics": [ "slem", "slem,markovchain-method" ] }, { "page": "spectralGap", "title": "Spectral gap of a Markov chain", "topics": [ "spectralGap", "spectralGap,markovchain-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": "subchain", "title": "Restrict a Markov chain to a subset of states", "topics": [ "subchain", "subchain,markovchain-method" ] }, { "page": "tauchen", "title": "Discretize an AR(1) process into a Markov chain (Tauchen's method)", "topics": [ "tauchen" ] }, { "page": "timeCorrelations", "title": "Time correlations and time relaxations of observed sequences", "topics": [ "timeCorrelations", "timeCorrelations,markovchain-method", "timeRelaxations", "timeRelaxations,markovchain-method" ] }, { "page": "tm_abs", "title": "Single Year Corporate Credit Rating Transititions", "topics": [ "tm_abs" ] }, { "page": "toBoundedChain", "title": "Apply a boundary condition to a Markov chain's first and last state", "topics": [ "toBoundedChain", "toBoundedChain,markovchain-method" ] }, { "page": "toDictionary", "title": "Represent a Markov chain as a plain R list", "topics": [ "fromDictionary", "toDictionary", "toDictionary,markovchain-method" ] }, { "page": "toDot", "title": "Export the transition graph as Graphviz DOT or Mermaid text", "topics": [ "toDot", "toDot,markovchain-method", "toMermaid", "toMermaid,markovchain-method" ] }, { "page": "toFile", "title": "Write or read a Markov chain to or from a file", "topics": [ "fromFile", "toFile", "toFile,markovchain-method" ] }, { "page": "toNthOrder", "title": "Return the n-step transition chain", "topics": [ "toNthOrder" ] }, { "page": "topologicalEntropy", "title": "Topological entropy of a Markov chain", "topics": [ "topologicalEntropy", "topologicalEntropy,markovchain-method" ] }, { "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": "urnModel", "title": "Build an Ehrenfest urn model Markov chain", "topics": [ "urnModel" ] }, { "page": "statisticalTests", "title": "Test the first-order Markov property of an empirical sequence", "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": "0.3.32+ds-5", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libblas", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.32+ds-5", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "16-20260322-1ubuntu1", "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", "Entropy rate", "Topological entropy and normalized entropy rate", "Classification of states", "First passage time distributions and means", "Kemeny's constant", "Sensitivity of the stationary distribution", "Spectral diagnostics: SLEM, spectral gap and implied timescales", "Reversibility", "Closest reversible approximation", "Mixing time", "Lazy chains", "Evolution of a distribution", "Mean recurrence time", "Absorption probabilities and mean absorption time", "Fundamental matrix", "Restricting a chain to a subset of states", "Committor probability", "Hitting probabilities", "Stochastic monotonicity", "Classical Markov chain constructors", "Birth-death chains", "The Ehrenfest urn model", "Combining two chains", "The n-step chain", "Population genetics: the Moran and Wright-Fisher models", "Random and special chains", "Statistical analysis", "Simulation", "Lumpability and aggregation of states", "Spectral Kullback-Leibler aggregation", "Serialization and export", "Estimation", "Prediction", "Predicting from a markovchain object", "Predicting from a markovchainList object", "Statistical tests", "Assessing the independence of consecutive states", "Assessing the Markov property of a Markov chain sequence", "Assessing the order of a Markov chain sequence", "Assessing the stationarity and time-homogeneity of a Markov chain sequence", "Divergence tests for empirically estimated transition matrices", "Pseudo-Bayesian estimation", "Bayesian estimation", "Notation and set-up", "Methods", "Predictive distribution", "Choosing the hyper-parameters", "Usage and examples", "Continuous time Markov chains", "Definition and generator matrix", "Stationary distributions", "Estimation of the generator matrix", "Expected hitting time", "Transition probabilities over time", "Examples", "Applications", "Weather forecasting", "Land of Oz", "Alofi Island Rainfall", "Finance and Economics", "Finance", "Economics", "Discretizing autoregressive processes", "Actuarial science", "MTPL 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": "2026-10-08 21:35:32", "commits": 60 }, { "source": "gsoc_2017_additions.qmd", "filename": "gsoc_2017_additions.html", "title": "Google Summer of Code 2017 Additions", "engine": "quarto::html", "headings": [ "Expected hitting time of a CTMC", "Probability at time t of a CTMC", "Plotting the generator matrix of a CTMC", "Imprecise continuous time Markov chains", "Types of ICTMCs", "Lower Transition Rate Operators for ICTMCs", "Lower Transition Operators", "The impreciseProbabilityatT function", "Generator of a CTMC from a frequency matrix", "Committor of a Markov chain", "First passage probability for a set of states", "Mean occupation of the states during the first N steps", "Expected rewards of a Markov chain", "Expected rewards before hitting a set of states", "Irreducibility of a CTMC", "Simulation of higher order multivariate Markov chains", "Time reversibility of a CTMC", "References" ], "created": "2026-08-15 22:07:57", "modified": "2026-10-08 21:35:32", "commits": 3 }, { "source": "higher_order_markov_chains.qmd", "filename": "higher_order_markov_chains.html", "title": "Higher order Markov chains", "engine": "quarto::html", "headings": [ "Higher order Markov chains as mixtures of lags", "Comparing models of different orders", "Selecting the order of a full Markov chain", "Reproducing a published comparison", "The mixture transition distribution model", "Prediction and simulation", "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", "Acknowledgments", "References" ], "created": "2026-08-15 22:07:57", "modified": "2026-10-08 21:35:32", "commits": 9 } ], "_score": 13.280944581943603, "_indexed": true, "_nocasepkg": "markovchain", "_universes": [ "spedygiorgio" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.2", "date": "2026-10-10T14:02:31.000Z", "distro": "resolute", "arch": "aarch64", "commit": "a796eb9486027099ca7a2b648b39623f7d4f0a87", "fileid": "https://r2.ropensci.org/7af5fc9898b8607f792f58f72fbff41edf799493965fdb3298c920c5a65902ad", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/spedygiorgio/actions/runs/38057510217" }, { "r": "4.7.0", "os": "linux", "version": "1.2", "date": "2026-10-10T14:03:16.000Z", "distro": "resolute", "arch": "x86_64", 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