| Mark-Recapture Distance Sampling (mrds) | mrds-package mrds |
| Add covariate levels detection function plots | add.df.covar.line add_df_covar_line |
| Check order of adjustment terms | adj.check.order |
| Cosine adjustment term, not the series. | adj.cos |
| Hermite polynomial adjustment term, not the series. | adj.herm |
| Simple polynomial adjustment term, not the series. | adj.poly |
| Series of the gradient of the cosine adjustment series w.r.t. the scaled distance. | adj.series.grad.cos |
| Series of the gradient of the Hermite polynomial adjustment series w.r.t. the scaled distance. | adj.series.grad.herm |
| Series of the gradient of the simple polynomial adjustment series w.r.t. the scaled distance. | adj.series.grad.poly |
| Akaike's An Information Criterion for detection functions | AIC.ddf AIC.ds AIC.io AIC.io.fi AIC.rem AIC.rem.fi AIC.trial AIC.trial.fi |
| Get the apex for a gamma detection function | apex.gamma |
| Assign default values to list elements that have not been already assigned | assign.default.values |
| Average detection function line for plotting | average.line |
| Average conditional detection function line for plotting | average.line.cond |
| Golf tee data used in chapter 6 of Advanced Distance Sampling examples | book.tee.data |
| Find se of average p and N | calc.se.Np |
| Cumulative distribution function (cdf) for fitted distance sampling detection function | cdf.ds |
| CDS function definition | cds |
| Check parameters bounds during optimisations | check.bounds |
| Check that a detection function is monotone | check.mono |
| Extract coefficients | coef.ds coef.io coef.io.fi coef.rem coef.rem.fi coef.trial coef.trial.fi coefficients |
| Horvitz-Thompson estimates 1/p_i or s_i/p_i | compute.Nht |
| Covered region estimate of abundance from Horvitz-Thompson-like estimator | covered.region.dht |
| Create bins from a set of binned distances and a set of cutpoints. | create.bins |
| create.command.file | create.command.file |
| Create a model frame for ddf fitting | create.model.frame |
| Creates structures needed to compute abundance and variance | create.varstructure |
| Distance Detection Function Fitting | ddf |
| CDS/MCDS Distance Detection Function Fitting | ddf.ds |
| Goodness of fit tests for distance sampling models | ddf.gof gof.io gof.io.fi gof.rem gof.rem.fi gof.trial gof.trial.fi |
| Mark-Recapture Distance Sampling (MRDS) IO - PI | ddf.io |
| Mark-Recapture Distance Sampling (MRDS) IO - FI | ddf.io.fi |
| Mark-Recapture Distance Sampling (MRDS) Removal - PI | ddf.rem |
| Mark-Recapture Distance Sampling (MRDS) Removal - FI | ddf.rem.fi |
| Mark-Recapture Distance Sampling (MRDS) Trial Configuration - PI | ddf.trial |
| Mark-Recapture Analysis of Trial Configuration - FI | ddf.trial.fi |
| Numeric Delta Method approximation for the variance-covariance matrix | DeltaMethod |
| Observation detection tables | det.tables |
| Fit detection function using key-adjustment functions | detfct.fit |
| Fit detection function using key-adjustment functions | detfct.fit.opt |
| Density and abundance estimates and variances | dht |
| Computes abundance estimates at specified parameter values using Horvitz-Thompson-like estimator | dht.deriv |
| Variance and confidence intervals for density and abundance estimates | dht.se |
| Gradient of the non-normalised pdf of distances or the detection function for the distances. | distpdf.grad |
| Distance Sampling Functions | ds.function |
| Log-likelihood computation for distance sampling data | flnl flpt.lnl |
| (Negative) gradients of constraint function | flnl.constr.grad.neg |
| Gradient of the negative log likelihood function | flnl.grad |
| Hessian computation for fitted distance detection function model parameters | flt.var |
| Compute value of p(0) using a logit formulation | g0 |
| Extraction and assignment of parameters to vector | getpar |
| Compute chi-square goodness-of-fit test for ds models | gof.ds |
| Integral of pdf of distances | gstdint |
| Plot histogram line | histline |
| Integrate a logistic detection function | integratedetfct.logistic |
| Analytically integrate logistic detection function | integratelogistic.analytic |
| Numerically integrate pdf of observed distances over specified ranges | integratepdf |
| Numerically integrates the non-normalised pdf or the detection function of observed distances over specified ranges. | integratepdf.grad |
| Iterative offset GLM/GAM for fitting detection function | io.glm |
| Collection of functions for logistic detection functions | is.linear.logistic |
| Is a logit model constant for all observations? | is.logistic.constant |
| The gradient of the half-normal key function | keyfct.grad.hn |
| The gradient of the hazard-rate key function | keyfct.grad.hz |
| Threshold key function | keyfct.th1 |
| Threshold key function | keyfct.th2 |
| Two-part normal key function | keyfct.tpn two-part-normal |
| Black-capped vireo mark-recapture distance sampling analysis | lfbcvi |
| Golden-cheeked warbler mark-recapture distance sampling analysis | lfgcwa |
| Logistic as a function of covariates | logisticbyx |
| Logistic as a function of distance | logisticbyz |
| Logistic detection function | logisticdetfct |
| Logistic for duplicates as a function of covariates | logisticdupbyx |
| Logistic for duplicates as a function of covariates (fast) | logisticdupbyx_fast |
| Logit function | logit |
| log-likelihood value for a fitted detection function | logLik.ddf logLik.ds logLik.io logLik.io.fi logLik.rem logLik.rem.fi logLik.trial logLik.trial.fi |
| MCDS function definition | mcds |
| Run MCDS.exe as a backend for mrds | MCDS MCDS.exe mcds_dot_exe |
| Tips on optimisation issues in 'mrds' models | mrds_opt |
| Compute estimated abundance in covered (sampled) region | NCovered NCovered.ds NCovered.io NCovered.io.fi NCovered.rem NCovered.rem.fi NCovered.trial NCovered.trial.fi |
| Wrapper around 'nlminb' | nlminb_wrapper |
| Double-platform detection probability | p.det |
| Distribution of probabilities of detection | p.dist.table p_dist_table |
| Parse optimx results and present a nice object | parse.optimx |
| Compute probability that a object was detected by at least one observer | pdot.dsr.integrate.logistic |
| Plot conditional detection function from distance sampling model | plot_cond |
| Layout for plot methods in mrds | plot_layout |
| Plot unconditional detection function from distance sampling model | plot_uncond |
| Observation detection tables | plot.det.tables |
| Plot fit of detection functions and histograms of data from distance sampling model | plot.ds |
| Plot fit of detection functions and histograms of data from distance sampling independent observer ('io') model | plot.io |
| Plot fit of detection functions and histograms of data from distance sampling independent observer model with full independence ('io.fi') | plot.io.fi |
| Plot fit of detection functions and histograms of data from removal distance sampling model | plot.rem |
| Plot fit of detection functions and histograms of data from removal distance sampling model | plot.rem.fi |
| Plot fit of detection functions and histograms of data from distance sampling trial observer model | plot.trial |
| Plot fit of detection functions and histograms of data from distance sampling trial observer model | plot.trial.fi |
| Predictions from 'mrds' models | predict predict.ddf predict.ds predict.io predict.io.fi predict.rem predict.rem.fi predict.trial predict.trial.fi |
| Simple pretty printer for distance sampling analyses | print.ddf |
| Prints results of goodness of fit tests for detection functions | print.ddf.gof |
| Print results of observer detection tables | print.det.tables |
| Prints density and abundance estimates | print.dht |
| Print distribution of probabilities of detection | print.p_dist_table |
| Print summary of distance detection function model object | print.summary.ds |
| Print summary of distance detection function model object | print.summary.io |
| Print summary of distance detection function model object | print.summary.io.fi |
| Print summary of distance detection function model object | print.summary.rem |
| Print summary of distance detection function model object | print.summary.rem.fi |
| Print summary of distance detection function model object | print.summary.trial |
| Print summary of distance detection function model object | print.summary.trial.fi |
| Derivatives for variance of average p and average p(0) variance | prob.deriv |
| Average p and average p(0) variance | prob.se |
| Process data for fitting distance sampling detection function | process.data |
| Pronghorn aerial survey data from Wyoming | pronghorn |
| Single observer point count data example from Distance | ptdata.distance |
| Simulated dual observer point count data | ptdata.dual |
| Simulated removal observer point count data | ptdata.removal |
| Simulated single observer point count data | ptdata.single |
| Quantile-quantile plot and goodness of fit tests for detection functions | qqplot.ddf |
| Iterative offset model fitting of mark-recapture with removal model | rem.glm |
| Calculate the parameter rescaling for parameters associated with covariates | rescale_pars |
| Generate data from a fitted detection function and refit the model | sample_ddf |
| Set parameter bounds | setbounds |
| Creates design matrix for covariates in detection function | setcov |
| Set initial values for detection function based on distance sampling | sethazard setinitial.ds |
| 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. | sim.mix |
| Invert of covariance matrices | solvecov |
| Wooden stake data from 1977 survey | stake77 |
| Wooden stake data from 1978 survey | stake78 |
| Summary of distance detection function model object | summary.ds |
| Summary of distance detection function model object | summary.io |
| Summary of distance detection function model object | summary.io.fi |
| Summary of distance detection function model object | summary.rem |
| Summary of distance detection function model object | summary.rem.fi |
| Summary of distance detection function model object | summary.trial |
| Summary of distance detection function model object | summary.trial.fi |
| Extrapolate Horvitz-Thompson abundance estimates to entire surveyed region | survey.region.dht |
| Test validity for histogram breaks(cutpoints) | test.breaks |
| Compute empirical variance of encounter rate | covn varn |
