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  "Title": "Non-Parametric Bayesian Analyses of Animal Movement",
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  "Description": "Methods for assessing animal movement from telemetry and\nbiologging data using non-parametric Bayesian methods. This\nincludes features for pre- processing and analysis of data, as\nwell as the visualization of results from the models. This\nframework does not rely on standard parametric density\nfunctions, which provides flexibility during model fitting.\nFurther details regarding part of this framework can be found\nin Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.",
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  "URL": "https://github.com/joshcullen/bayesmove,\nhttps://joshcullen.github.io/bayesmove/",
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  "Language": "en-US",
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  "Repository": "https://joshcullen.r-universe.dev",
  "Date/Publication": "2026-01-09 18:51:25 UTC",
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  "Author": "Joshua Cullen [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0002-6935-9340>),\nDenis Valle [aut, cph]",
  "Maintainer": "Joshua Cullen <joshcullen10@gmail.com>",
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    "segment_behavior",
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    "traceplot"
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        "date",
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      "object": "tracks.seg",
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      "page": "assign_behavior",
      "title": "Assign behavior estimates to observations",
      "topics": [
        "assign_behavior"
      ]
    },
    {
      "page": "assign_tseg",
      "title": "Add segment numbers to observations",
      "topics": [
        "assign_tseg"
      ]
    },
    {
      "page": "assign_tseg_internal",
      "title": "Internal function that adds segment numbers to observations",
      "topics": [
        "assign_tseg_internal"
      ]
    },
    {
      "page": "behav_gibbs_sampler",
      "title": "Internal function that runs RJMCMC on a single animal ID",
      "topics": [
        "behav_gibbs_sampler"
      ]
    },
    {
      "page": "behav_seg_image",
      "title": "Internal function that transforms a vector of bin numbers to a presence-absence matrix",
      "topics": [
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    {
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      "title": "Cluster observations into behavioral states",
      "topics": [
        "cluster_obs"
      ]
    },
    {
      "page": "cluster_segments",
      "title": "Cluster time segments into behavioral states",
      "topics": [
        "cluster_segments"
      ]
    },
    {
      "page": "CumSumInv",
      "title": "Internal function that calculates the inverted cumsum",
      "topics": [
        "CumSumInv"
      ]
    },
    {
      "page": "df_to_list",
      "title": "Convert data frame to a list by animal ID",
      "topics": [
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      ]
    },
    {
      "page": "discrete_move_var",
      "title": "Discretize movement variables",
      "topics": [
        "discrete_move_var"
      ]
    },
    {
      "page": "expand_behavior",
      "title": "Expand behavior estimates from track segments to observations",
      "topics": [
        "expand_behavior"
      ]
    },
    {
      "page": "extract_prop",
      "title": "Extract behavior proportion estimates for each track segment",
      "topics": [
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      ]
    },
    {
      "page": "filter_time",
      "title": "Filter observations for time interval of interest",
      "topics": [
        "filter_time"
      ]
    },
    {
      "page": "find_breaks",
      "title": "Find changes for integer variable",
      "topics": [
        "find_breaks"
      ]
    },
    {
      "page": "get_behav_hist",
      "title": "Extract bin estimates from Latent Dirichlet Allocation or mixture model",
      "topics": [
        "get_behav_hist"
      ]
    },
    {
      "page": "get_breakpts",
      "title": "Extract breakpoints for each animal ID",
      "topics": [
        "get_breakpts"
      ]
    },
    {
      "page": "get_MAP",
      "title": "Find the maximum a posteriori (MAP) estimate of the MCMC chain",
      "topics": [
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      ]
    },
    {
      "page": "get_summary_stats",
      "title": "Internal function that calculates the sufficient statistics for the segmentation model",
      "topics": [
        "get_summary_stats"
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    },
    {
      "page": "get.llk.mixmod",
      "title": "Internal function to calculate the log-likelihood for iteration of mixture model",
      "topics": [
        "get.llk.mixmod"
      ]
    },
    {
      "page": "get.theta",
      "title": "Internal function to calculate theta parameter",
      "topics": [
        "get.theta"
      ]
    },
    {
      "page": "insert_NAs",
      "title": "Insert NA gaps to regularize a time series",
      "topics": [
        "insert_NAs"
      ]
    },
    {
      "page": "log_marg_likel",
      "title": "Internal function that calculates the log marginal likelihood of each model being compared",
      "topics": [
        "log_marg_likel"
      ]
    },
    {
      "page": "plot_breakpoints",
      "title": "Plot breakpoints over a time series of each movement variable",
      "topics": [
        "plot_breakpoints"
      ]
    },
    {
      "page": "plot_breakpoints_behav",
      "title": "Internal function for plotting breakpoints over each of the data streams",
      "topics": [
        "plot_breakpoints_behav"
      ]
    },
    {
      "page": "prep_data",
      "title": "Calculate step lengths, turning angles, net-squared displacement, and time steps",
      "topics": [
        "prep_data"
      ]
    },
    {
      "page": "prep_data_internal",
      "title": "Internal function to calculate step lengths, turning angles, and time steps",
      "topics": [
        "prep_data_internal"
      ]
    },
    {
      "page": "rmultinom1",
      "title": "Internal function that samples z's from a categorical distribution",
      "topics": [
        "rmultinom1"
      ]
    },
    {
      "page": "rmultinom2",
      "title": "Internal function that samples z's from a multinomial distribution",
      "topics": [
        "rmultinom2"
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    },
    {
      "page": "round_track_time",
      "title": "Round time to nearest interval",
      "topics": [
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      ]
    },
    {
      "page": "samp_move",
      "title": "Internal function for the Gibbs sampler within the reversible-jump MCMC algorithm",
      "topics": [
        "samp_move"
      ]
    },
    {
      "page": "sample.gamma.mixmod",
      "title": "Internal function to sample the gamma hyperparameter",
      "topics": [
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      ]
    },
    {
      "page": "sample.phi",
      "title": "Internal function to sample bin estimates for each movement variable",
      "topics": [
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    },
    {
      "page": "sample.phi.mixmod",
      "title": "Internal function to sample bin estimates for each movement variable",
      "topics": [
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    },
    {
      "page": "sample.v",
      "title": "Internal function to sample parameter for truncated stick-breaking prior",
      "topics": [
        "sample.v"
      ]
    },
    {
      "page": "sample.v.mixmod",
      "title": "Internal function to sample parameter for truncated stick-breaking prior",
      "topics": [
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      ]
    },
    {
      "page": "sample.z",
      "title": "Internal function to sample latent clusters",
      "topics": [
        "sample.z"
      ]
    },
    {
      "page": "sample.z.mixmod",
      "title": "Internal function to sample latent clusters (for observations)",
      "topics": [
        "sample.z.mixmod"
      ]
    },
    {
      "page": "SampleZAgg",
      "title": "Internal function that samples z1 aggregate",
      "topics": [
        "SampleZAgg"
      ]
    },
    {
      "page": "segment_behavior",
      "title": "Segmentation model to estimate breakpoints",
      "topics": [
        "segment_behavior"
      ]
    },
    {
      "page": "shiny_tracks",
      "title": "Dynamically explore tracks within Shiny app",
      "topics": [
        "shiny_tracks"
      ]
    },
    {
      "page": "StoreZ",
      "title": "This function helps store z from all iterations after burn in",
      "topics": [
        "StoreZ"
      ]
    },
    {
      "page": "summarize_tsegs",
      "title": "Summarize observations within bins per track segment",
      "topics": [
        "summarize_tsegs"
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    },
    {
      "page": "summarize1",
      "title": "Internal function that summarizes bin distributions of track segments",
      "topics": [
        "summarize1"
      ]
    },
    {
      "page": "SummarizeDat",
      "title": "Internal function that generates nmat matrix to help with multinomial draws",
      "topics": [
        "SummarizeDat"
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    },
    {
      "page": "traceplot",
      "title": "View trace-plots of output from Bayesian segmentation model",
      "topics": [
        "traceplot"
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    },
    {
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      "topics": [
        "tracks"
      ]
    },
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      "page": "tracks.seg",
      "title": "Segmented tracks for all IDs.",
      "topics": [
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