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  "Description": "Animal abundance estimation via conventional, multiple\ncovariate and mark-recapture distance sampling (CDS/MCDS/MRDS).\nDetection function fitting is performed via maximum likelihood.\nAlso included are diagnostics and plotting for fitted detection\nfunctions. Abundance estimation is via a Horvitz-Thompson-like\nestimator.",
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      "title": "Mark-Recapture Distance Sampling (mrds)",
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        "mrds"
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      "page": "add.df.covar.line",
      "title": "Add covariate levels detection function plots",
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      "title": "Hermite polynomial adjustment term, not the series.",
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      "title": "Simple polynomial adjustment term, not the series.",
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      "topics": [
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      "topics": [
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      "title": "Akaike's An Information Criterion for detection functions",
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        "AIC.ds",
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        "AIC.io.fi",
        "AIC.rem",
        "AIC.rem.fi",
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      "title": "Get the apex for a gamma detection function",
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      "title": "Assign default values to list elements that have not been already assigned",
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      "title": "Average detection function line for plotting",
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      "title": "Average conditional detection function line for plotting",
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      "topics": [
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      "topics": [
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      "title": "Cumulative distribution function (cdf) for fitted distance sampling detection function",
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      "topics": [
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      "page": "check.bounds",
      "title": "Check parameters bounds during optimisations",
      "topics": [
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      "title": "Check that a detection function is monotone",
      "topics": [
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      "title": "Extract coefficients",
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        "coef.io",
        "coef.io.fi",
        "coef.rem",
        "coef.rem.fi",
        "coef.trial",
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        "coefficients"
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    {
      "page": "compute.Nht",
      "title": "Horvitz-Thompson estimates 1/p_i or s_i/p_i",
      "topics": [
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    },
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      "title": "Covered region estimate of abundance from Horvitz-Thompson-like estimator",
      "topics": [
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    },
    {
      "page": "create.bins",
      "title": "Create bins from a set of binned distances and a set of cutpoints.",
      "topics": [
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    },
    {
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      "title": "create.command.file",
      "topics": [
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    },
    {
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      "title": "Create a model frame for ddf fitting",
      "topics": [
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    },
    {
      "page": "create.varstructure",
      "title": "Creates structures needed to compute abundance and variance",
      "topics": [
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    },
    {
      "page": "ddf",
      "title": "Distance Detection Function Fitting",
      "topics": [
        "ddf"
      ]
    },
    {
      "page": "ddf.ds",
      "title": "CDS/MCDS Distance Detection Function Fitting",
      "topics": [
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    },
    {
      "page": "ddf.gof",
      "title": "Goodness of fit tests for distance sampling models",
      "topics": [
        "ddf.gof",
        "gof.io",
        "gof.io.fi",
        "gof.rem",
        "gof.rem.fi",
        "gof.trial",
        "gof.trial.fi"
      ]
    },
    {
      "page": "ddf.io",
      "title": "Mark-Recapture Distance Sampling (MRDS) IO - PI",
      "topics": [
        "ddf.io"
      ]
    },
    {
      "page": "ddf.io.fi",
      "title": "Mark-Recapture Distance Sampling (MRDS) IO - FI",
      "topics": [
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    },
    {
      "page": "ddf.rem",
      "title": "Mark-Recapture Distance Sampling (MRDS) Removal - PI",
      "topics": [
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    },
    {
      "page": "ddf.rem.fi",
      "title": "Mark-Recapture Distance Sampling (MRDS) Removal - FI",
      "topics": [
        "ddf.rem.fi"
      ]
    },
    {
      "page": "ddf.trial",
      "title": "Mark-Recapture Distance Sampling (MRDS) Trial Configuration - PI",
      "topics": [
        "ddf.trial"
      ]
    },
    {
      "page": "ddf.trial.fi",
      "title": "Mark-Recapture Analysis of Trial Configuration - FI",
      "topics": [
        "ddf.trial.fi"
      ]
    },
    {
      "page": "DeltaMethod",
      "title": "Numeric Delta Method approximation for the variance-covariance matrix",
      "topics": [
        "DeltaMethod"
      ]
    },
    {
      "page": "det.tables",
      "title": "Observation detection tables",
      "topics": [
        "det.tables"
      ]
    },
    {
      "page": "detfct.fit",
      "title": "Fit detection function using key-adjustment functions",
      "topics": [
        "detfct.fit"
      ]
    },
    {
      "page": "detfct.fit.opt",
      "title": "Fit detection function using key-adjustment functions",
      "topics": [
        "detfct.fit.opt"
      ]
    },
    {
      "page": "dht",
      "title": "Density and abundance estimates and variances",
      "topics": [
        "dht"
      ]
    },
    {
      "page": "dht.deriv",
      "title": "Computes abundance estimates at specified parameter values using Horvitz-Thompson-like estimator",
      "topics": [
        "dht.deriv"
      ]
    },
    {
      "page": "dht.se",
      "title": "Variance and confidence intervals for density and abundance estimates",
      "topics": [
        "dht.se"
      ]
    },
    {
      "page": "distpdf.grad",
      "title": "Gradient of the non-normalised pdf of distances or the detection function for the distances.",
      "topics": [
        "distpdf.grad"
      ]
    },
    {
      "page": "ds.function",
      "title": "Distance Sampling Functions",
      "topics": [
        "ds.function"
      ]
    },
    {
      "page": "flnl",
      "title": "Log-likelihood computation for distance sampling data",
      "topics": [
        "flnl",
        "flpt.lnl"
      ]
    },
    {
      "page": "flnl.constr.grad.neg",
      "title": "(Negative) gradients of constraint function",
      "topics": [
        "flnl.constr.grad.neg"
      ]
    },
    {
      "page": "flnl.grad",
      "title": "Gradient of the negative log likelihood function",
      "topics": [
        "flnl.grad"
      ]
    },
    {
      "page": "flt.var",
      "title": "Hessian computation for fitted distance detection function model parameters",
      "topics": [
        "flt.var"
      ]
    },
    {
      "page": "g0",
      "title": "Compute value of p(0) using a logit formulation",
      "topics": [
        "g0"
      ]
    },
    {
      "page": "getpar",
      "title": "Extraction and assignment of parameters to vector",
      "topics": [
        "getpar"
      ]
    },
    {
      "page": "gof.ds",
      "title": "Compute chi-square goodness-of-fit test for ds models",
      "topics": [
        "gof.ds"
      ]
    },
    {
      "page": "gstdint",
      "title": "Integral of pdf of distances",
      "topics": [
        "gstdint"
      ]
    },
    {
      "page": "histline",
      "title": "Plot histogram line",
      "topics": [
        "histline"
      ]
    },
    {
      "page": "integratedetfct.logistic",
      "title": "Integrate a logistic detection function",
      "topics": [
        "integratedetfct.logistic"
      ]
    },
    {
      "page": "integratelogistic.analytic",
      "title": "Analytically integrate logistic detection function",
      "topics": [
        "integratelogistic.analytic"
      ]
    },
    {
      "page": "integratepdf",
      "title": "Numerically integrate pdf of observed distances over specified ranges",
      "topics": [
        "integratepdf"
      ]
    },
    {
      "page": "integratepdf.grad",
      "title": "Numerically integrates the non-normalised pdf or the detection function of observed distances over specified ranges.",
      "topics": [
        "integratepdf.grad"
      ]
    },
    {
      "page": "io.glm",
      "title": "Iterative offset GLM/GAM for fitting detection function",
      "topics": [
        "io.glm"
      ]
    },
    {
      "page": "is.linear.logistic",
      "title": "Collection of functions for logistic detection functions",
      "topics": [
        "is.linear.logistic"
      ]
    },
    {
      "page": "is.logistic.constant",
      "title": "Is a logit model constant for all observations?",
      "topics": [
        "is.logistic.constant"
      ]
    },
    {
      "page": "keyfct.grad.hn",
      "title": "The gradient of the half-normal key function",
      "topics": [
        "keyfct.grad.hn"
      ]
    },
    {
      "page": "keyfct.grad.hz",
      "title": "The gradient of the hazard-rate key function",
      "topics": [
        "keyfct.grad.hz"
      ]
    },
    {
      "page": "keyfct.th1",
      "title": "Threshold key function",
      "topics": [
        "keyfct.th1"
      ]
    },
    {
      "page": "keyfct.th2",
      "title": "Threshold key function",
      "topics": [
        "keyfct.th2"
      ]
    },
    {
      "page": "keyfct.tpn",
      "title": "Two-part normal key function",
      "topics": [
        "keyfct.tpn",
        "two-part-normal"
      ]
    },
    {
      "page": "lfbcvi",
      "title": "Black-capped vireo mark-recapture distance sampling analysis",
      "topics": [
        "lfbcvi"
      ]
    },
    {
      "page": "lfgcwa",
      "title": "Golden-cheeked warbler mark-recapture distance sampling analysis",
      "topics": [
        "lfgcwa"
      ]
    },
    {
      "page": "logisticbyx",
      "title": "Logistic as a function of covariates",
      "topics": [
        "logisticbyx"
      ]
    },
    {
      "page": "logisticbyz",
      "title": "Logistic as a function of distance",
      "topics": [
        "logisticbyz"
      ]
    },
    {
      "page": "logisticdetfct",
      "title": "Logistic detection function",
      "topics": [
        "logisticdetfct"
      ]
    },
    {
      "page": "logisticdupbyx",
      "title": "Logistic for duplicates as a function of covariates",
      "topics": [
        "logisticdupbyx"
      ]
    },
    {
      "page": "logisticdupbyx_fast",
      "title": "Logistic for duplicates as a function of covariates (fast)",
      "topics": [
        "logisticdupbyx_fast"
      ]
    },
    {
      "page": "logit",
      "title": "Logit function",
      "topics": [
        "logit"
      ]
    },
    {
      "page": "logLik.ddf",
      "title": "log-likelihood value for a fitted detection function",
      "topics": [
        "logLik.ddf",
        "logLik.ds",
        "logLik.io",
        "logLik.io.fi",
        "logLik.rem",
        "logLik.rem.fi",
        "logLik.trial",
        "logLik.trial.fi"
      ]
    },
    {
      "page": "mcds",
      "title": "MCDS function definition",
      "topics": [
        "mcds"
      ]
    },
    {
      "page": "mcds_dot_exe",
      "title": "Run MCDS.exe as a backend for mrds",
      "topics": [
        "MCDS",
        "MCDS.exe",
        "mcds_dot_exe"
      ]
    },
    {
      "page": "mrds_opt",
      "title": "Tips on optimisation issues in 'mrds' models",
      "topics": [
        "mrds_opt"
      ]
    },
    {
      "page": "NCovered",
      "title": "Compute estimated abundance in covered (sampled) region",
      "topics": [
        "NCovered",
        "NCovered.ds",
        "NCovered.io",
        "NCovered.io.fi",
        "NCovered.rem",
        "NCovered.rem.fi",
        "NCovered.trial",
        "NCovered.trial.fi"
      ]
    },
    {
      "page": "nlminb_wrapper",
      "title": "Wrapper around 'nlminb'",
      "topics": [
        "nlminb_wrapper"
      ]
    },
    {
      "page": "p.det",
      "title": "Double-platform detection probability",
      "topics": [
        "p.det"
      ]
    },
    {
      "page": "p.dist.table",
      "title": "Distribution of probabilities of detection",
      "topics": [
        "p.dist.table",
        "p_dist_table"
      ]
    },
    {
      "page": "parse.optimx",
      "title": "Parse optimx results and present a nice object",
      "topics": [
        "parse.optimx"
      ]
    },
    {
      "page": "pdot.dsr.integrate.logistic",
      "title": "Compute probability that a object was detected by at least one observer",
      "topics": [
        "pdot.dsr.integrate.logistic"
      ]
    },
    {
      "page": "plot_cond",
      "title": "Plot conditional detection function from distance sampling model",
      "topics": [
        "plot_cond"
      ]
    },
    {
      "page": "plot_layout",
      "title": "Layout for plot methods in mrds",
      "topics": [
        "plot_layout"
      ]
    },
    {
      "page": "plot_uncond",
      "title": "Plot unconditional detection function from distance sampling model",
      "topics": [
        "plot_uncond"
      ]
    },
    {
      "page": "plot.det.tables",
      "title": "Observation detection tables",
      "topics": [
        "plot.det.tables"
      ]
    },
    {
      "page": "plot.ds",
      "title": "Plot fit of detection functions and histograms of data from distance sampling model",
      "topics": [
        "plot.ds"
      ]
    },
    {
      "page": "plot.io",
      "title": "Plot fit of detection functions and histograms of data from distance sampling independent observer ('io') model",
      "topics": [
        "plot.io"
      ]
    },
    {
      "page": "plot.io.fi",
      "title": "Plot fit of detection functions and histograms of data from distance sampling independent observer model with full independence ('io.fi')",
      "topics": [
        "plot.io.fi"
      ]
    },
    {
      "page": "plot.rem",
      "title": "Plot fit of detection functions and histograms of data from removal distance sampling model",
      "topics": [
        "plot.rem"
      ]
    },
    {
      "page": "plot.rem.fi",
      "title": "Plot fit of detection functions and histograms of data from removal distance sampling model",
      "topics": [
        "plot.rem.fi"
      ]
    },
    {
      "page": "plot.trial",
      "title": "Plot fit of detection functions and histograms of data from distance sampling trial observer model",
      "topics": [
        "plot.trial"
      ]
    },
    {
      "page": "plot.trial.fi",
      "title": "Plot fit of detection functions and histograms of data from distance sampling trial observer model",
      "topics": [
        "plot.trial.fi"
      ]
    },
    {
      "page": "predict.ds",
      "title": "Predictions from 'mrds' models",
      "topics": [
        "predict",
        "predict.ddf",
        "predict.ds",
        "predict.io",
        "predict.io.fi",
        "predict.rem",
        "predict.rem.fi",
        "predict.trial",
        "predict.trial.fi"
      ]
    },
    {
      "page": "print.ddf",
      "title": "Simple pretty printer for distance sampling analyses",
      "topics": [
        "print.ddf"
      ]
    },
    {
      "page": "print.ddf.gof",
      "title": "Prints results of goodness of fit tests for detection functions",
      "topics": [
        "print.ddf.gof"
      ]
    },
    {
      "page": "print.det.tables",
      "title": "Print results of observer detection tables",
      "topics": [
        "print.det.tables"
      ]
    },
    {
      "page": "print.dht",
      "title": "Prints density and abundance estimates",
      "topics": [
        "print.dht"
      ]
    },
    {
      "page": "print.p_dist_table",
      "title": "Print distribution of probabilities of detection",
      "topics": [
        "print.p_dist_table"
      ]
    },
    {
      "page": "print.summary.ds",
      "title": "Print summary of distance detection function model object",
      "topics": [
        "print.summary.ds"
      ]
    },
    {
      "page": "print.summary.io",
      "title": "Print summary of distance detection function model object",
      "topics": [
        "print.summary.io"
      ]
    },
    {
      "page": "print.summary.io.fi",
      "title": "Print summary of distance detection function model object",
      "topics": [
        "print.summary.io.fi"
      ]
    },
    {
      "page": "print.summary.rem",
      "title": "Print summary of distance detection function model object",
      "topics": [
        "print.summary.rem"
      ]
    },
    {
      "page": "print.summary.rem.fi",
      "title": "Print summary of distance detection function model object",
      "topics": [
        "print.summary.rem.fi"
      ]
    },
    {
      "page": "print.summary.trial",
      "title": "Print summary of distance detection function model object",
      "topics": [
        "print.summary.trial"
      ]
    },
    {
      "page": "print.summary.trial.fi",
      "title": "Print summary of distance detection function model object",
      "topics": [
        "print.summary.trial.fi"
      ]
    },
    {
      "page": "prob.deriv",
      "title": "Derivatives for variance of average p and average p(0) variance",
      "topics": [
        "prob.deriv"
      ]
    },
    {
      "page": "prob.se",
      "title": "Average p and average p(0) variance",
      "topics": [
        "prob.se"
      ]
    },
    {
      "page": "process.data",
      "title": "Process data for fitting distance sampling detection function",
      "topics": [
        "process.data"
      ]
    },
    {
      "page": "pronghorn",
      "title": "Pronghorn aerial survey data from Wyoming",
      "topics": [
        "pronghorn"
      ]
    },
    {
      "page": "ptdata.distance",
      "title": "Single observer point count data example from Distance",
      "topics": [
        "ptdata.distance"
      ]
    },
    {
      "page": "ptdata.dual",
      "title": "Simulated dual observer point count data",
      "topics": [
        "ptdata.dual"
      ]
    },
    {
      "page": "ptdata.removal",
      "title": "Simulated removal observer point count data",
      "topics": [
        "ptdata.removal"
      ]
    },
    {
      "page": "ptdata.single",
      "title": "Simulated single observer point count data",
      "topics": [
        "ptdata.single"
      ]
    },
    {
      "page": "qqplot.ddf",
      "title": "Quantile-quantile plot and goodness of fit tests for detection functions",
      "topics": [
        "qqplot.ddf"
      ]
    },
    {
      "page": "rem.glm",
      "title": "Iterative offset model fitting of mark-recapture with removal model",
      "topics": [
        "rem.glm"
      ]
    },
    {
      "page": "rescale_pars",
      "title": "Calculate the parameter rescaling for parameters associated with covariates",
      "topics": [
        "rescale_pars"
      ]
    },
    {
      "page": "sample_ddf",
      "title": "Generate data from a fitted detection function and refit the model",
      "topics": [
        "sample_ddf"
      ]
    },
    {
      "page": "setbounds",
      "title": "Set parameter bounds",
      "topics": [
        "setbounds"
      ]
    },
    {
      "page": "setcov",
      "title": "Creates design matrix for covariates in detection function",
      "topics": [
        "setcov"
      ]
    },
    {
      "page": "setinitial.ds",
      "title": "Set initial values for detection function based on distance sampling",
      "topics": [
        "sethazard",
        "setinitial.ds"
      ]
    },
    {
      "page": "sim.mix",
      "title": "Simulation of distance sampling data via mixture models Allows one to simulate line transect distance sampling data using a mixture of half-normal detection functions.",
      "topics": [
        "sim.mix"
      ]
    },
    {
      "page": "solvecov",
      "title": "Invert of covariance matrices",
      "topics": [
        "solvecov"
      ]
    },
    {
      "page": "stake77",
      "title": "Wooden stake data from 1977 survey",
      "topics": [
        "stake77"
      ]
    },
    {
      "page": "stake78",
      "title": "Wooden stake data from 1978 survey",
      "topics": [
        "stake78"
      ]
    },
    {
      "page": "summary.ds",
      "title": "Summary of distance detection function model object",
      "topics": [
        "summary.ds"
      ]
    },
    {
      "page": "summary.io",
      "title": "Summary of distance detection function model object",
      "topics": [
        "summary.io"
      ]
    },
    {
      "page": "summary.io.fi",
      "title": "Summary of distance detection function model object",
      "topics": [
        "summary.io.fi"
      ]
    },
    {
      "page": "summary.rem",
      "title": "Summary of distance detection function model object",
      "topics": [
        "summary.rem"
      ]
    },
    {
      "page": "summary.rem.fi",
      "title": "Summary of distance detection function model object",
      "topics": [
        "summary.rem.fi"
      ]
    },
    {
      "page": "summary.trial",
      "title": "Summary of distance detection function model object",
      "topics": [
        "summary.trial"
      ]
    },
    {
      "page": "summary.trial.fi",
      "title": "Summary of distance detection function model object",
      "topics": [
        "summary.trial.fi"
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    },
    {
      "page": "survey.region.dht",
      "title": "Extrapolate Horvitz-Thompson abundance estimates to entire surveyed region",
      "topics": [
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      ]
    },
    {
      "page": "test.breaks",
      "title": "Test validity for histogram breaks(cutpoints)",
      "topics": [
        "test.breaks"
      ]
    },
    {
      "page": "varn",
      "title": "Compute empirical variance of encounter rate",
      "topics": [
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        "varn"
      ]
    }
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      "fileid": "7d0818e7c4028537c186849ca9601018c298a96d18a482f15239d42ee34cd168",
      "status": "success",
      "buildurl": "https://github.com/r-universe/distancedevelopment/actions/runs/25847901964"
    }
  ]
}