{
  "_id": "6a10576facfb0bcc41ca2fb3",
  "Package": "LDATS",
  "Title": "Latent Dirichlet Allocation Coupled with Time Series Analyses",
  "Version": "0.2.7",
  "Authors@R": "c(\nperson(c(\"Juniper\", \"L.\"), \"Simonis\",\nemail = \"juniper.simonis@weecology.org\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9798-0460\")),\nperson(c(\"Erica\", \"M.\"), \"Christensen\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0002-5635-2502\")),\nperson(c(\"David\", \"J.\"), \"Harris\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-3332-9307\")),\nperson(c(\"Renata\", \"M.\"), \"Diaz\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-0803-4734\")),\nperson(\"Hao\", \"Ye\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-8630-1458\")),\nperson(c(\"Ethan\", \"P.\"), \"White\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-6728-7745\")),\nperson(c(\"S.K.\", \"Morgan\"), \"Ernest\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-6026-8530\")),\nperson(c(\"Weecology\"), role = \"cph\"))",
  "Description": "Combines Latent Dirichlet Allocation (LDA) and Bayesian\nmultinomial time series methods in a two-stage analysis to\nquantify dynamics in high-dimensional temporal data. LDA\ndecomposes multivariate data into lower-dimension latent\ngroupings, whose relative proportions are modeled using\ngeneralized Bayesian time series models that include abrupt\nchangepoints and smooth dynamics. The methods are described in\nBlei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western\nand Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and\nRipley (2002, ISBN-13:978-0387954578), and Christensen et al.\n(2018) <doi:10.1002/ecy.2373>.",
  "URL": "https://weecology.github.io/LDATS,\nhttps://github.com/weecology/LDATS",
  "BugReports": "https://github.com/weecology/LDATS/issues",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "VignetteBuilder": "knitr",
  "RoxygenNote": "6.1.1",
  "Config/pak/sysreqs": "libgsl0-dev libxml2-dev",
  "Repository": "https://weecology.r-universe.dev",
  "Date/Publication": "2020-03-19 08:56:46 UTC",
  "RemoteUrl": "https://github.com/weecology/ldats",
  "RemoteRef": "HEAD",
  "RemoteSha": "d48386296a217a81ebba1950a46d04c40f60b50b",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-09 07:23:59 UTC",
    "User": "root"
  },
  "Author": "Juniper L. Simonis [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-9798-0460>),\nErica M. Christensen [aut] (ORCID:\n<https://orcid.org/0000-0002-5635-2502>),\nDavid J. Harris [aut] (ORCID: <https://orcid.org/0000-0003-3332-9307>),\nRenata M. Diaz [aut] (ORCID: <https://orcid.org/0000-0003-0803-4734>),\nHao Ye [aut] (ORCID: <https://orcid.org/0000-0002-8630-1458>),\nEthan P. White [aut] (ORCID: <https://orcid.org/0000-0001-6728-7745>),\nS.K. Morgan Ernest [aut] (ORCID:\n<https://orcid.org/0000-0002-6026-8530>),\nWeecology [cph]",
  "Maintainer": "Juniper L. Simonis <juniper.simonis@weecology.org>",
  "MD5sum": "cf7fb3ac5d76b44945a7121ba980152f",
  "_user": "weecology",
  "_type": "src",
  "_file": "LDATS_0.2.7.tar.gz",
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  "_created": "2026-05-09T07:23:59.000Z",
  "_published": "2026-05-22T13:17:35.630Z",
  "_distro": "noble",
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  "_status": "success",
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    "name": "Juniper L. Simonis",
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  "_owner": "weecology",
  "_selfowned": true,
  "_usedby": 1,
  "_updates": [],
  "_tags": [],
  "_topics": [
    "changepoint",
    "lda",
    "parallel-tempering",
    "portal",
    "softmax"
  ],
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  "_contributors": [
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    "name": "Weecology"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/LDATS"
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  "_pkgdown": "https://weecology.github.io/LDATS",
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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    "extra/NEWS.html",
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    "extra/readme.md",
    "manual.pdf"
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  "_homeurl": "https://github.com/weecology/ldats",
  "_realowner": "weecology",
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  "_releases": [
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      "date": "2019-08-01"
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    {
      "version": "0.2.7",
      "date": "2020-03-19"
    },
    {
      "version": "0.3.0",
      "date": "2023-09-19"
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  ],
  "_exports": [
    "AICc",
    "autocorr_plot",
    "check_changepoints",
    "check_control",
    "check_document_covariate_table",
    "check_document_term_table",
    "check_formula",
    "check_formulas",
    "check_LDA_models",
    "check_LDA_set_inputs",
    "check_LDA_TS_inputs",
    "check_multinom_TS_inputs",
    "check_nchangepoints",
    "check_seeds",
    "check_timename",
    "check_topics",
    "check_TS_inputs",
    "check_TS_on_LDA_inputs",
    "check_weights",
    "conform_LDA_TS_data",
    "count_trips",
    "diagnose_ptMCMC",
    "document_weights",
    "ecdf_plot",
    "est_changepoints",
    "est_regressors",
    "eta_diagnostics_plots",
    "eval_step",
    "expand_TS",
    "iftrue",
    "LDA_msg",
    "LDA_plot_bottom_panel",
    "LDA_plot_top_panel",
    "LDA_set",
    "LDA_set_control",
    "LDA_TS",
    "LDA_TS_control",
    "logsumexp",
    "measure_eta_vcov",
    "measure_rho_vcov",
    "memoise_fun",
    "messageq",
    "mirror_vcov",
    "modalvalue",
    "multinom_TS",
    "multinom_TS_chunk",
    "normalize",
    "package_chunk_fits",
    "package_LDA_set",
    "package_LDA_TS",
    "package_TS",
    "package_TS_on_LDA",
    "posterior_plot",
    "pred_gamma_TS_plot",
    "prep_chunks",
    "prep_cpts",
    "prep_ids",
    "prep_LDA_control",
    "prep_pbar",
    "prep_proposal_dist",
    "prep_ptMCMC_inputs",
    "prep_saves",
    "prep_temp_sequence",
    "prep_TS_data",
    "print_model_run_message",
    "process_saves",
    "propose_step",
    "proposed_step_mods",
    "rho_diagnostics_plots",
    "rho_hist",
    "rho_lines",
    "select_LDA",
    "select_TS",
    "set_gamma_colors",
    "set_LDA_plot_colors",
    "set_LDA_TS_plot_cols",
    "set_rho_hist_colors",
    "set_TS_summary_plot_cols",
    "sim_LDA_data",
    "sim_LDA_TS_data",
    "sim_TS_data",
    "softmax",
    "step_chains",
    "summarize_etas",
    "summarize_rhos",
    "swap_chains",
    "take_step",
    "trace_plot",
    "TS",
    "TS_control",
    "TS_diagnostics_plot",
    "TS_on_LDA",
    "TS_summary_plot",
    "update_cpts",
    "update_ids",
    "update_pbar",
    "update_saves",
    "verify_changepoint_locations"
  ],
  "_datasets": [
    {
      "name": "jornada",
      "title": "Jornada rodent data",
      "object": "jornada",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "rodents",
      "title": "Portal rodent data",
      "object": "rodents",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "AICc",
      "title": "Calculate AICc",
      "topics": [
        "AICc"
      ]
    },
    {
      "page": "autocorr_plot",
      "title": "Produce the autocorrelation panel for the TS diagnostic plot of a parameter",
      "topics": [
        "autocorr_plot"
      ]
    },
    {
      "page": "check_changepoints",
      "title": "Check that a set of change point locations is proper",
      "topics": [
        "check_changepoints"
      ]
    },
    {
      "page": "check_control",
      "title": "Check that a control list is proper",
      "topics": [
        "check_control"
      ]
    },
    {
      "page": "check_document_covariate_table",
      "title": "Check that the document covariate table is proper",
      "topics": [
        "check_document_covariate_table"
      ]
    },
    {
      "page": "check_document_term_table",
      "title": "Check that document term table is proper",
      "topics": [
        "check_document_term_table"
      ]
    },
    {
      "page": "check_formula",
      "title": "Check that a formula is proper",
      "topics": [
        "check_formula"
      ]
    },
    {
      "page": "check_formulas",
      "title": "Check that formulas vector is proper and append the response variable",
      "topics": [
        "check_formulas"
      ]
    },
    {
      "page": "check_LDA_models",
      "title": "Check that LDA model input is proper",
      "topics": [
        "check_LDA_models"
      ]
    },
    {
      "page": "check_nchangepoints",
      "title": "Check that nchangepoints vector is proper",
      "topics": [
        "check_nchangepoints"
      ]
    },
    {
      "page": "check_seeds",
      "title": "Check that nseeds value or seeds vector is proper",
      "topics": [
        "check_seeds"
      ]
    },
    {
      "page": "check_timename",
      "title": "Check that the time vector is proper",
      "topics": [
        "check_timename"
      ]
    },
    {
      "page": "check_topics",
      "title": "Check that topics vector is proper",
      "topics": [
        "check_topics"
      ]
    },
    {
      "page": "check_weights",
      "title": "Check that weights vector is proper",
      "topics": [
        "check_weights"
      ]
    },
    {
      "page": "count_trips",
      "title": "Count trips of the ptMCMC particles",
      "topics": [
        "count_trips"
      ]
    },
    {
      "page": "diagnose_ptMCMC",
      "title": "Calculate ptMCMC summary diagnostics",
      "topics": [
        "diagnose_ptMCMC"
      ]
    },
    {
      "page": "document_weights",
      "title": "Calculate document weights for a corpus",
      "topics": [
        "document_weights"
      ]
    },
    {
      "page": "ecdf_plot",
      "title": "Produce the posterior distribution ECDF panel for the TS diagnostic plot of a parameter",
      "topics": [
        "ecdf_plot"
      ]
    },
    {
      "page": "est_changepoints",
      "title": "Use ptMCMC to estimate the distribution of change point locations",
      "topics": [
        "est_changepoints"
      ]
    },
    {
      "page": "est_regressors",
      "title": "Estimate the distribution of regressors, unconditional on the change point locations",
      "topics": [
        "est_regressors"
      ]
    },
    {
      "page": "expand_TS",
      "title": "Expand the TS models across the factorial combination of LDA models, formulas, and number of change points",
      "topics": [
        "expand_TS"
      ]
    },
    {
      "page": "iftrue",
      "title": "Replace if TRUE",
      "topics": [
        "iftrue"
      ]
    },
    {
      "page": "jornada",
      "title": "Jornada rodent data",
      "topics": [
        "jornada"
      ]
    },
    {
      "page": "LDA_msg",
      "title": "Create the model-running-message for an LDA",
      "topics": [
        "LDA_msg"
      ]
    },
    {
      "page": "LDA_set",
      "title": "Run a set of Latent Dirichlet Allocation models",
      "topics": [
        "check_LDA_set_inputs",
        "LDA_set"
      ]
    },
    {
      "page": "LDA_set_control",
      "title": "Create control list for set of LDA models",
      "topics": [
        "LDA_set_control"
      ]
    },
    {
      "page": "LDA_TS",
      "title": "Run a full set of Latent Dirichlet Allocations and Time Series models",
      "topics": [
        "check_LDA_TS_inputs",
        "conform_LDA_TS_data",
        "LDA_TS"
      ]
    },
    {
      "page": "LDA_TS_control",
      "title": "Create the controls list for the LDATS model",
      "topics": [
        "LDA_TS_control"
      ]
    },
    {
      "page": "LDATS",
      "title": "Package to conduct two-stage analyses combining Latent Dirichlet Allocation with Bayesian Time Series models",
      "topics": [
        "LDATS-package",
        "LDATS"
      ]
    },
    {
      "page": "logLik.LDA_VEM",
      "title": "Calculate the log likelihood of a VEM LDA model fit",
      "topics": [
        "logLik.LDA_VEM"
      ]
    },
    {
      "page": "logLik.multinom_TS_fit",
      "title": "Log likelihood of a multinomial TS model",
      "topics": [
        "logLik.multinom_TS_fit"
      ]
    },
    {
      "page": "logLik.TS_fit",
      "title": "Determine the log likelihood of a Time Series model",
      "topics": [
        "logLik.TS_fit"
      ]
    },
    {
      "page": "logsumexp",
      "title": "Calculate the log-sum-exponential (LSE) of a vector",
      "topics": [
        "logsumexp"
      ]
    },
    {
      "page": "memoise_fun",
      "title": "Logical control on whether or not to memoise",
      "topics": [
        "memoise_fun"
      ]
    },
    {
      "page": "messageq",
      "title": "Optionally generate a message based on a logical input",
      "topics": [
        "messageq"
      ]
    },
    {
      "page": "mirror_vcov",
      "title": "Create a properly symmetric variance covariance matrix",
      "topics": [
        "mirror_vcov"
      ]
    },
    {
      "page": "modalvalue",
      "title": "Determine the mode of a distribution",
      "topics": [
        "modalvalue"
      ]
    },
    {
      "page": "multinom_TS",
      "title": "Fit a multinomial change point Time Series model",
      "topics": [
        "check_multinom_TS_inputs",
        "multinom_TS"
      ]
    },
    {
      "page": "multinom_TS_chunk",
      "title": "Fit a multinomial Time Series model chunk",
      "topics": [
        "multinom_TS_chunk"
      ]
    },
    {
      "page": "normalize",
      "title": "Normalize a vector",
      "topics": [
        "normalize"
      ]
    },
    {
      "page": "package_chunk_fits",
      "title": "Package the output of the chunk-level multinomial models into a multinom_TS_fit list",
      "topics": [
        "package_chunk_fits"
      ]
    },
    {
      "page": "package_LDA_set",
      "title": "Package the output from LDA_set",
      "topics": [
        "package_LDA_set"
      ]
    },
    {
      "page": "package_LDA_TS",
      "title": "Package the output of LDA_TS",
      "topics": [
        "package_LDA_TS"
      ]
    },
    {
      "page": "package_TS",
      "title": "Summarize the Time Series model",
      "topics": [
        "package_TS"
      ]
    },
    {
      "page": "package_TS_on_LDA",
      "title": "Package the output of TS_on_LDA",
      "topics": [
        "package_TS_on_LDA"
      ]
    },
    {
      "page": "plot.LDA_set",
      "title": "Plot a set of LDATS LDA models",
      "topics": [
        "plot.LDA_set"
      ]
    },
    {
      "page": "plot.LDA_TS",
      "title": "Plot the key results from a full LDATS analysis",
      "topics": [
        "plot.LDA_TS"
      ]
    },
    {
      "page": "plot.LDA_VEM",
      "title": "Plot the results of an LDATS LDA model",
      "topics": [
        "LDA_plot_bottom_panel",
        "LDA_plot_top_panel",
        "plot.LDA_VEM"
      ]
    },
    {
      "page": "plot.TS_fit",
      "title": "Plot an LDATS TS model",
      "topics": [
        "plot.TS_fit"
      ]
    },
    {
      "page": "posterior_plot",
      "title": "Produce the posterior distribution histogram panel for the TS diagnostic plot of a parameter",
      "topics": [
        "posterior_plot"
      ]
    },
    {
      "page": "prep_chunks",
      "title": "Prepare the time chunk table for a multinomial change point Time Series model",
      "topics": [
        "prep_chunks"
      ]
    },
    {
      "page": "prep_cpts",
      "title": "Initialize and update the change point matrix used in the ptMCMC algorithm",
      "topics": [
        "prep_cpts",
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      "title": "Portal rodent data",
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    {
      "page": "select_LDA",
      "title": "Select the best LDA model(s) for use in time series",
      "topics": [
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      "page": "set_LDA_plot_colors",
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      "topics": [
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    {
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      "page": "trace_plot",
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      "topics": [
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