{ "_id": "6a1823adacfb0bcc41db39f7", "Package": "LaMa", "Type": "Package", "Title": "Fast Numerical Maximum Likelihood Estimation for Latent Markov\nModels", "Version": "2.1.1", "Authors@R": "c(\nperson(given = \"Jan-Ole\", family = \"Fischer\",\nemail = \"jan-ole.fischer@mailbox.org\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0004-1556-9053\")))", "Description": "A variety of latent Markov models, including hidden Markov\nmodels, hidden semi-Markov models, state-space models and\ncontinuous-time variants can be formulated and estimated within\nthe same framework via directly maximising the likelihood\nfunction using the so-called forward algorithm. Applied\nresearchers often need custom models that standard software\ndoes not easily support. Writing tailored 'R' code offers\nflexibility but suffers from slow estimation speeds. We address\nthese issues by providing easy-to-use functions (written in\n'C++' for speed) for common tasks like the forward algorithm.\nThese functions can be combined into custom models in a\nLego-type approach, offering up to 10-20 times faster\nestimation via standard numerical optimisers. To aid in\nbuilding fully custom likelihood functions, several vignettes\nare included that show how to simulate data from and estimate\nall the above model classes.", "URL": "https://janolefi.github.io/LaMa/", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "LazyData": "true", "Repository": "https://janolefi.r-universe.dev", "Date/Publication": "2026-05-28 10:18:47 UTC", "RemoteUrl": "https://github.com/janolefi/lama", "RemoteRef": "HEAD", "RemoteSha": "7035273e0e481e3767ff7ec555b69be27651a1f9", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-28 11:01:55 UTC", "User": "root" }, "Author": "Jan-Ole Fischer [aut, cre] (ORCID:\n)", "Maintainer": "Jan-Ole Fischer ", "MD5sum": "3a645933a08d88e1f50dab880ae33b0e", "_user": "janolefi", "_type": "src", "_file": "LaMa_2.1.1.tar.gz", "_fileid": "782dc3993f7d367c3bd5c396181fac6a4f7b84cdbf0a2854693f412403b8504b", "_filesize": 6507389, "_sha256": 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distribution", "topics": [ "dgmrf2" ] }, { "page": "forward", "title": "Forward algorithm to calculate the HMM log-likelihood", "concept": [ "forward algorithms" ], "topics": [ "forward" ] }, { "page": "forward_g", "title": "Forward algorithm with time-varying transition probability matrix", "concept": [ "forward algorithms" ], "topics": [ "forward_g" ] }, { "page": "forward_hsmm", "title": "Forward algorithm for homogeneous hidden semi-Markov models", "concept": [ "forward algorithms" ], "topics": [ "forward_hsmm" ] }, { "page": "forward_ihsmm", "title": "Forward algorithm for hidden semi-Markov models with inhomogeneous state durations and/ or conditional transition probabilities", "concept": [ "forward algorithms" ], "topics": [ "forward_ihsmm" ] }, { "page": "forward_p", "title": "Forward algorithm with for periodically varying transition probability matrices", "concept": [ "forward algorithms" ], "topics": [ "forward_p" ] }, { "page": "forward_phsmm", "title": "Forward algorithm for hidden semi-Markov models with periodically inhomogeneous state durations and/ or conditional transition probabilities", "concept": [ "forward algorithms" ], "topics": [ "forward_phsmm" ] }, { "page": "forward_s", "title": "Forward algorithm for hidden semi-Markov models with homogeneous transition probability matrix", "topics": [ "forward_s" ] }, { "page": "forward_sp", "title": "Forward algorithm for hidden semi-Markov models with periodically varying transition probability matrices", "topics": [ "forward_sp" ] }, { "page": "gamma2", "title": "Reparametrised gamma distribution", "topics": [ "dgamma2", "gamma2", "pgamma2", "qgamma2", "rgamma2" ] }, { "page": "gdeterminant", "title": "Computes generalised determinant", "topics": [ "gdeterminant" ] }, { "page": "generator", "title": "Build the generator matrix of a continuous-time Markov chain", "concept": [ "transition probability matrix functions" ], "topics": [ "generator" ] }, { "page": "generator_g", "title": "Build generator matrices of a continuous-time Markov chain", "concept": [ "transition probability matrix functions" ], "topics": [ "generator_g" ] }, { "page": "LaMaColors", "title": "Generate a colour-blind-friendly palette", "topics": [ "LaMaColors" ] }, { "page": "logLik.LaMaModel", "title": "Extract log-likelihood from LaMaModel object", "topics": [ "logLik.LaMaModel" ] }, { "page": "logLik.qremlModel", "title": "Extract log-likelihood from qremlModel object", "topics": [ "logLik.qremlModel" ] }, { "page": "make_matrices", "title": "Build the design and the penalty matrix for models involving penalised splines based on a formula and a data set", "topics": [ "make_matrices" ] }, { "page": "make_matrices_dens", "title": "Build a standardised P-Spline design matrix and the associated P-Spline penalty matrix", "topics": [ "make_matrices_dens" ] }, { "page": "make_matrices_old", "title": "Build the design and the penalty matrix for models involving penalised splines based on a formula and a data set", "topics": [ "make_matrices_old" ] }, { "page": "MCreport", "title": "Sample parameters from approximate Gaussian posterior distribution", "topics": [ "MCreport" ] }, { "page": "minmax", "title": "AD-compatible minimum and maximum functions", "topics": [ "max2", "min2", "minmax" ] }, { "page": "minmax0_smooth", "title": "Smooth approximations to max(x, 0) and min(x, 0)", "topics": [ "max0_smooth", "min0_smooth", "minmax0_smooth" ] }, { "page": "nessi", "title": "Loch Ness Monster Acceleration Data", "topics": [ "nessi" ] }, { "page": "penalty", "title": "Computes penalty based on quadratic form", "topics": [ "penalty" ] }, { "page": "penalty_uni", "title": "Penalty approximation of unimodality constraints for univariates smooths", "topics": [ "penalty_uni" ] }, { "page": "penalty2", "title": "Computes generalised quadratic-form penalties", "topics": [ "penalty2" ] }, { "page": "plot.LaMaResiduals", "title": "Plot pseudo-residuals", "topics": [ "plot.LaMaResiduals" ] }, { "page": "pred_matrix", "title": "Build the prediction design matrix based on new data and model_matrices object created by 'make_matrices'", "topics": [ "pred_matrix" ] }, { "page": "predict.LaMa_matrices", "title": "Build the prediction design matrix based on new data and model_matrices object created by 'make_matrices'", "topics": [ "predict.LaMa_matrices" ] }, { "page": "process_hid_formulas", "title": "Process and standardise formulas for the state process of hidden Markov models", "topics": [ "process_hid_formulas" ] }, { "page": "pseudo_res", "title": "Calculate pseudo-residuals", "topics": [ "pseudo_res" ] }, { "page": "qreml", "title": "Quasi restricted maximum likelihood (qREML) algorithm for models with penalised splines or simple i.i.d. random effects", "topics": [ "qreml" ] }, { "page": "qreml_old", "title": "Quasi restricted maximum likelihood (qREML) algorithm for models with penalised splines or simple i.i.d. random effects", "topics": [ "qreml_old" ] }, { "page": "report", "title": "Get reported quantities from and 'RTMB' object and return a 'LaMaModel'", "topics": [ "report" ] }, { "page": "sdreport_outer", "title": "Report uncertainty of the estimated smoothing parameters or variances", "topics": [ "sdreport_outer" ] }, { "page": "sdreportMC", "title": "Monte Carlo version of 'sdreport'", "topics": [ "sdreportMC" ] }, { "page": "skewnorm", "title": "Skew normal distribution", "topics": [ "dskewnorm", "pskewnorm", "qskewnorm", "rskewnorm", "skewnorm" ] }, { "page": "smooth_dens_construct", "title": "Build the design and penalty matrices for smooth density estimation", "topics": [ "smooth_dens_construct" ] }, { "page": "stateprobs", "title": "Calculate conditional local state probabilities in HMMs", "concept": [ "decoding functions" ], "topics": [ "stateprobs" ] }, { "page": "stateprobs_g", "title": "Calculate conditional local state probabilities for inhomogeneous HMMs", "concept": [ "decoding functions" ], "topics": [ "stateprobs_g" ] }, { "page": "stateprobs_p", "title": "Calculate conditional local state probabilities for periodically inhomogeneous HMMs", "concept": [ "decoding functions" ], "topics": [ "stateprobs_p" ] }, { "page": "stationary", "title": "Compute the stationary distribution of a homogeneous Markov chain", "concept": [ "stationary distribution functions" ], "topics": [ "stationary" ] }, { "page": "stationary_ct", "title": "Compute the stationary distribution of a continuous-time Markov chain", "concept": [ "stationary distribution functions" ], "topics": [ "stationary_cont", "stationary_ct" ] }, { "page": "stationary_p", "title": "Periodically stationary distribution of a periodically inhomogeneous Markov chain", "concept": [ "stationary distribution functions" ], "topics": [ "stationary_p" ] }, { "page": "stationary_p_sparse", "title": "Sparse version of 'stationary_p'", "topics": [ "stationary_p_sparse" ] }, { "page": "stationary_sparse", "title": "Sparse version of 'stationary'", "topics": [ "stationary_sparse" ] }, { "page": "summary.qremlModel", "title": "Summary method for 'qremlModel' objects", "topics": [ "summary.qremlModel" ] }, { "page": "tpm", "title": "Build the transition probability matrix from unconstrained parameter vector", "concept": [ "transition probability matrix functions" ], "topics": [ "tpm" ] }, { "page": "tpm_ct", "title": "Calculate continuous time transition probabilities", "concept": [ "transition probability matrix functions" ], "topics": [ "tpm_cont", "tpm_ct" ] }, { "page": "tpm_emb", "title": "Build the embedded transition probability matrix of an HSMM from unconstrained parameter vector", "concept": [ "transition probability matrix functions" ], "topics": [ "tpm_emb" ] }, { "page": "tpm_emb_g", "title": "Build all embedded transition probability matrices of an inhomogeneous HSMM", "concept": [ "transition probability matrix functions" ], "topics": [ "tpm_emb_g" ] }, { "page": "tpm_g", "title": "Build all transition probability matrices of an inhomogeneous HMM", "concept": [ "transition probability matrix functions" ], "topics": [ "tpm_g" ] }, { "page": "tpm_g2", "title": "Build all transition probability matrices of an inhomogeneous HMM", "concept": [ "transition probability matrix functions" ], "topics": [ "tpm_g2" ] }, { "page": "tpm_hsmm", "title": "Builds the transition probability matrix of an HSMM-approximating HMM", "topics": [ "tpm_hsmm" ] }, { "page": "tpm_hsmm2", "title": "Build the transition probability matrix of an HSMM-approximating HMM", "topics": [ "tpm_hsmm2" ] }, { "page": "tpm_ihsmm", "title": "Builds all transition probability matrices of an inhomogeneous-HSMM-approximating HMM", "topics": [ "tpm_ihsmm" ] }, { "page": "tpm_p", "title": "Build all transition probability matrices of a periodically inhomogeneous HMM", "concept": [ "transition probability matrix functions" ], "topics": [ "tpm_p" ] }, { "page": "tpm_phsmm", "title": "Builds all transition probability matrices of an periodic-HSMM-approximating HMM", "topics": [ "tpm_phsmm" ] }, { "page": "tpm_phsmm2", "title": "Build all transition probability matrices of an periodic-HSMM-approximating HMM", "topics": [ "tpm_phsmm2" ] }, { "page": "tpm_thinned", "title": "Compute the transition probability matrix of a thinned periodically inhomogeneous Markov chain.", "topics": [ "tpm_thinned" ] }, { "page": "trex", "title": "T-Rex Movement Data", "topics": [ "trex" ] }, { "page": "trigBasisExp", "title": "Compute the design matrix for a trigonometric basis expansion", "topics": [ "trigBasisExp" ] }, { "page": "viterbi", "title": "Viterbi algorithm for state decoding in HMMs", "concept": [ "decoding functions" ], "topics": [ "viterbi" ] }, { "page": "viterbi_g", "title": "Viterbi algorithm for state decoding in inhomogeneous HMMs", "concept": [ "decoding functions" ], "topics": [ "viterbi_g" ] }, { "page": "viterbi_p", "title": "Viterbi algorithm for state decoding in periodically inhomogeneous HMMs", "concept": [ "decoding functions" ], "topics": [ "viterbi_p" ] }, { "page": "vm", "title": "Von Mises distribution", "topics": [ "dvm", "pvm", "rvm", "vm" ] }, { "page": "wrpcauchy", "title": "Wrapped Cauchy distribution", "topics": [ "dwrpcauchy", "rwrpcauchy", "wrpcauchy" ] }, { "page": "zero_inflate", "title": "Zero-inflated density constructer", "topics": [ "zero_inflate" ] } ], "_readme": "https://github.com/janolefi/lama/raw/HEAD/README.md", "_rundeps": [ "boot", "circular", "clue", "cluster", "gamlss.dist", "lattice", "MASS", "Matrix", "MatrixModels", "mgcv", "mnormt", "movMF", "mvtnorm", "nlme", "numDeriv", "quantreg", "Rcpp", "RcppArmadillo", "RcppEigen", "RTMB", "RTMBdist", "skmeans", "slam", "sn", "SparseM", "splines2", "statmod", "survival", "TMB" ], "_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) 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