{
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  "Date": "2025-04-02",
  "Title": "Mark-Recapture Analysis for Survival and Abundance Estimation",
  "Author": "Jeff Laake <jefflaake@gmail.com>, Devin Johnson\n<devin.johnson@noaa.gov>, Paul Conn <paul.conn@noaa.gov>,\nexample for simHMM from Jay Rotella",
  "Maintainer": "Jeff Laake <jefflaake@gmail.com>",
  "Description": "Functions for fitting various models to capture-recapture\ndata including mixed-effects Cormack-Jolly-Seber(CJS) and\nmultistate models and the multi-variate state model structure\nfor survival estimation and POPAN structured Jolly-Seber models\nfor abundance estimation. There are also Hidden Markov model\n(HMM) implementations of CJS and multistate models with and\nwithout state uncertainty and a simulation capability for HMM\nmodels.",
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    "description": "PhD from Colorado State University in Wildlife Biology with an emphasis on statistical modelling/software development; retired but  still developing software",
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    {
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    "backward_prob",
    "cjs_delta",
    "cjs_dmat",
    "cjs_gamma",
    "cjs.hessian",
    "collapseCH",
    "compute_matrices",
    "compute_real",
    "create.model.list",
    "crm",
    "crm.wrapper",
    "crmlist_fromfiles",
    "dmat_hsmm2hmm",
    "fix.parameters",
    "function.wrapper",
    "fx.aic",
    "fx.par.count",
    "global_decode",
    "hmmDemo",
    "hsmm2hmm",
    "initiate_pi",
    "js.hessian",
    "load.model",
    "local_decode",
    "loglikelihood",
    "make.design.data",
    "mcmc_mode",
    "merge_design.covariates",
    "model.table",
    "ms_dmat",
    "ms_gamma",
    "ms2_gamma",
    "mscjs",
    "mscjs_tmb",
    "msld_tmb",
    "mvms_design_data",
    "mvms_dmat",
    "mvmscjs",
    "mvmscjs_delta",
    "mvmscjs_tmb",
    "naive.survival",
    "omega",
    "p.boxplot",
    "p.mean",
    "Phi.boxplot",
    "Phi.mean",
    "probitCJS",
    "process.ch",
    "process.data",
    "R_HMMLikelihood",
    "rerun_crm",
    "resight.matrix",
    "set_mvms",
    "setup_admb",
    "setup_tmb",
    "setup.model",
    "setup.parameters",
    "simHMM",
    "smsld_tmb",
    "splitCH",
    "ums_dmat",
    "ums2_dmat"
  ],
  "_datasets": [
    {
      "name": "dipper",
      "title": "Dipper capture-recapture data",
      "object": "dipper",
      "file": "dipper.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ch",
        "sex"
      ],
      "rows": 294,
      "table": true,
      "tojson": true
    },
    {
      "name": "mstrata",
      "title": "Multistrata example data",
      "object": "mstrata",
      "file": "mstrata.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ch",
        "freq"
      ],
      "rows": 255,
      "table": true,
      "tojson": true
    },
    {
      "name": "ps",
      "title": "Mulstistate Live-Dead Paradise Shelduck Data",
      "object": "Paradise_shelduck",
      "file": "Paradise_shelduck.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ch",
        "freq",
        "sex"
      ],
      "rows": 1704,
      "table": true,
      "tojson": true
    },
    {
      "name": "sealions",
      "title": "Multivariate State example data",
      "object": "sealions",
      "file": "sealions.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ch",
        "sex",
        "weight"
      ],
      "rows": 485,
      "table": true,
      "tojson": true
    },
    {
      "name": "skagit",
      "title": "An example of the Mulstistrata (multi-state) model in which states are routes taken by migrating fish.",
      "object": "skagit",
      "file": "skagit.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ch",
        "tag"
      ],
      "rows": 100,
      "table": true,
      "tojson": true
    },
    {
      "name": "tagloss",
      "title": "Tag loss example",
      "object": "tagloss",
      "file": "tagloss.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ch",
        "perm"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "backward_prob",
      "title": "Computes backward probabilities",
      "topics": [
        "backward_prob"
      ]
    },
    {
      "page": "cjs_admb",
      "title": "Fitting function for CJS models",
      "topics": [
        "cjs_admb"
      ]
    },
    {
      "page": "cjs_delta",
      "title": "HMM Initial state distribution functions",
      "topics": [
        "cjs_delta",
        "mvmscjs_delta"
      ]
    },
    {
      "page": "cjs_gamma",
      "title": "HMM Transition matrix functions",
      "topics": [
        "cjs_gamma",
        "ms2_gamma",
        "ms_gamma"
      ]
    },
    {
      "page": "cjs_tmb",
      "title": "Fitting function for CJS models",
      "topics": [
        "cjs_tmb"
      ]
    },
    {
      "page": "cjs.accumulate",
      "title": "Accumulates common capture history values",
      "topics": [
        "cjs.accumulate"
      ]
    },
    {
      "page": "cjs.hessian",
      "title": "Compute variance-covariance matrix for fitted CJS model",
      "topics": [
        "cjs.hessian"
      ]
    },
    {
      "page": "cjs.initial",
      "title": "Computes starting values for CJS p and Phi parameters",
      "topics": [
        "cjs.initial"
      ]
    },
    {
      "page": "cjs.lnl",
      "title": "Likelihood function for Cormack-Jolly-Seber model",
      "topics": [
        "cjs.lnl"
      ]
    },
    {
      "page": "coef.crm",
      "title": "Extract coefficients",
      "topics": [
        "coef.crm"
      ]
    },
    {
      "page": "compute_matrices",
      "title": "Compute HMM matrices",
      "topics": [
        "compute_matrices"
      ]
    },
    {
      "page": "compute_real",
      "title": "Compute estimates of real parameters",
      "topics": [
        "compute_real"
      ]
    },
    {
      "page": "convert.link.to.real",
      "title": "Convert link values to real parameters",
      "topics": [
        "convert.link.to.real"
      ]
    },
    {
      "page": "create.dm",
      "title": "Creates a design matrix for a parameter",
      "topics": [
        "create.dm",
        "create.dml"
      ]
    },
    {
      "page": "create.dmdf",
      "title": "Creates a dataframe with all the design data for a particular parameter in a crm model",
      "topics": [
        "create.dmdf"
      ]
    },
    {
      "page": "create.fixed.matrix",
      "title": "Create parameters with fixed matrix",
      "topics": [
        "create.fixed.matrix"
      ]
    },
    {
      "page": "create.links",
      "title": "Creates a 0/1 vector for real parameters with sin link",
      "topics": [
        "create.links"
      ]
    },
    {
      "page": "crm",
      "title": "Capture-recapture model fitting function",
      "topics": [
        "crm"
      ]
    },
    {
      "page": "crm.wrapper",
      "title": "Automation of model runs",
      "topics": [
        "create.model.list",
        "crm.wrapper",
        "crmlist_fromfiles",
        "load.model",
        "model.table",
        "rerun_crm"
      ]
    },
    {
      "page": "deriv_inverse.link",
      "title": "Derivatives of inverse of link function (internal use)",
      "topics": [
        "deriv.inverse.link",
        "deriv_inverse.link"
      ]
    },
    {
      "page": "dipper",
      "title": "Dipper capture-recapture data",
      "topics": [
        "dipper"
      ]
    },
    {
      "page": "dmat_hsmm2hmm",
      "title": "Create expanded state-dependent observation matrix for HMM from HSMM",
      "topics": [
        "dmat_hsmm2hmm"
      ]
    },
    {
      "page": "fix.parameters",
      "title": "Fixing real parameters in crm models",
      "topics": [
        "fix.parameters"
      ]
    },
    {
      "page": "function.wrapper",
      "title": "Utility extract functions",
      "topics": [
        "function.wrapper",
        "fx.aic",
        "fx.par.count"
      ]
    },
    {
      "page": "global_decode",
      "title": "Global decoding of HMM",
      "topics": [
        "global_decode"
      ]
    },
    {
      "page": "hmmDemo",
      "title": "HMM computation demo functions",
      "topics": [
        "hmmDemo"
      ]
    },
    {
      "page": "HMMLikelihood",
      "title": "Hidden Markov Model likelihood functions",
      "topics": [
        "hmm.lnl",
        "HMMLikelihood",
        "reals"
      ]
    },
    {
      "page": "hsmm2hmm",
      "title": "Compute transition matrix for HMM from HSMM",
      "topics": [
        "hsmm2hmm"
      ]
    },
    {
      "page": "initiate_pi",
      "title": "Setup fixed values for pi in design data",
      "topics": [
        "initiate_pi"
      ]
    },
    {
      "page": "inverse.link",
      "title": "Inverse link functions (internal use)",
      "topics": [
        "inverse.link"
      ]
    },
    {
      "page": "js",
      "title": "Fitting function for Jolly-Seber model using Schwarz-Arnason POPAN formulation",
      "topics": [
        "js"
      ]
    },
    {
      "page": "js.accumulate",
      "title": "Accumulates common capture history values",
      "topics": [
        "js.accumulate"
      ]
    },
    {
      "page": "js.hessian",
      "title": "Compute variance-covariance matrix for fitted JS model",
      "topics": [
        "js.hessian"
      ]
    },
    {
      "page": "js.lnl",
      "title": "Likelihood function for Jolly-Seber model using Schwarz-Arnason POPAN formulation",
      "topics": [
        "js.lnl"
      ]
    },
    {
      "page": "local_decode",
      "title": "Local decoding of HMM",
      "topics": [
        "local_decode"
      ]
    },
    {
      "page": "make.design.data",
      "title": "Create design dataframes for crm",
      "topics": [
        "make.design.data"
      ]
    },
    {
      "page": "merge_design.covariates",
      "title": "Merge time (occasion) and/or group specific covariates into design data",
      "topics": [
        "merge.design.covariates",
        "merge_design.covariates"
      ]
    },
    {
      "page": "mixed.model.admb",
      "title": "Mixed effect model contstruction",
      "topics": [
        "mixed.model",
        "mixed.model.admb",
        "mixed.model.dat",
        "reindex"
      ]
    },
    {
      "page": "mscjs",
      "title": "Fitting function for Multistate CJS models",
      "topics": [
        "mscjs"
      ]
    },
    {
      "page": "mscjs_tmb",
      "title": "Fitting function for Multistate CJS models with TMB",
      "topics": [
        "mscjs_tmb"
      ]
    },
    {
      "page": "msld_tmb",
      "title": "Fitting function for Multistate CJS live-dead models with TMB",
      "topics": [
        "msld_tmb"
      ]
    },
    {
      "page": "mstrata",
      "title": "Multistrata example data",
      "topics": [
        "mstrata"
      ]
    },
    {
      "page": "mvms_design_data",
      "title": "Multivariate Multistate (mvms) Design Data",
      "topics": [
        "mvms_design_data"
      ]
    },
    {
      "page": "mvms_dmat",
      "title": "HMM Observation Probability matrix functions",
      "topics": [
        "cjs_dmat",
        "ms_dmat",
        "mvms_dmat",
        "ums2_dmat",
        "ums_dmat"
      ]
    },
    {
      "page": "mvmscjs",
      "title": "Fitting function for Multivariate Multistate CJS with uncertainty models",
      "topics": [
        "mvmscjs"
      ]
    },
    {
      "page": "mvmscjs_tmb",
      "title": "TMB version: Fitting function for Multivariate Multistate CJS with uncertainty models",
      "topics": [
        "mvmscjs_tmb"
      ]
    },
    {
      "page": "omega",
      "title": "Compute 1 to k-step transition proportions",
      "topics": [
        "omega"
      ]
    },
    {
      "page": "Paradise_shelduck",
      "title": "Mulstistate Live-Dead Paradise Shelduck Data",
      "topics": [
        "Paradise_shelduck",
        "ps"
      ]
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