| add.df.covar.line |
Add covariate levels detection function plots |
| add_df_covar_line |
Add covariate levels detection function plots |
| adj.check.order |
Check order of adjustment terms |
| AIC.ddf |
Akaike's An Information Criterion for detection functions |
| AIC.ds |
Akaike's An Information Criterion for detection functions |
| AIC.io |
Akaike's An Information Criterion for detection functions |
| AIC.io.fi |
Akaike's An Information Criterion for detection functions |
| AIC.rem |
Akaike's An Information Criterion for detection functions |
| AIC.rem.fi |
Akaike's An Information Criterion for detection functions |
| AIC.trial |
Akaike's An Information Criterion for detection functions |
| AIC.trial.fi |
Akaike's An Information Criterion for detection functions |
| apex.gamma |
Get the apex for a gamma detection function |
| assign.default.values |
Assign default values to list elements that have not been already assigned |
| average.line |
Average detection function line for plotting |
| average.line.cond |
Average conditional detection function line for plotting |
| ddf |
Distance Detection Function Fitting |
| ddf.ds |
CDS/MCDS Distance Detection Function Fitting |
| ddf.gof |
Goodness of fit tests for distance sampling models |
| ddf.io |
Mark-Recapture Distance Sampling (MRDS) IO - PI |
| ddf.io.fi |
Mark-Recapture Distance Sampling (MRDS) IO - FI |
| ddf.rem |
Mark-Recapture Distance Sampling (MRDS) Removal - PI |
| ddf.rem.fi |
Mark-Recapture Distance Sampling (MRDS) Removal - FI |
| ddf.trial |
Mark-Recapture Distance Sampling (MRDS) Trial Configuration - PI |
| ddf.trial.fi |
Mark-Recapture Analysis of Trial Configuration - FI |
| DeltaMethod |
Numeric Delta Method approximation for the variance-covariance matrix |
| det.tables |
Observation detection tables |
| detfct.fit |
Fit detection function using key-adjustment functions |
| detfct.fit.opt |
Fit detection function using key-adjustment functions |
| dht |
Density and abundance estimates and variances |
| dht.deriv |
Computes abundance estimates at specified parameter values using Horvitz-Thompson-like estimator |
| dht.se |
Variance and confidence intervals for density and abundance estimates |
| ds.function |
Distance Sampling Functions |
| g0 |
Compute value of p(0) using a logit formulation |
| getpar |
Extraction and assignment of parameters to vector |
| gof.ds |
Compute chi-square goodness-of-fit test for ds models |
| gof.io |
Goodness of fit tests for distance sampling models |
| gof.io.fi |
Goodness of fit tests for distance sampling models |
| gof.rem |
Goodness of fit tests for distance sampling models |
| gof.rem.fi |
Goodness of fit tests for distance sampling models |
| gof.trial |
Goodness of fit tests for distance sampling models |
| gof.trial.fi |
Goodness of fit tests for distance sampling models |
| gstdint |
Integral of pdf of distances |
| lfbcvi |
Black-capped vireo mark-recapture distance sampling analysis |
| lfgcwa |
Golden-cheeked warbler mark-recapture distance sampling analysis |
| logisticbyx |
Logistic as a function of covariates |
| logisticbyz |
Logistic as a function of distance |
| logisticdetfct |
Logistic detection function |
| logisticdupbyx |
Logistic for duplicates as a function of covariates |
| logisticdupbyx_fast |
Logistic for duplicates as a function of covariates (fast) |
| logit |
Logit function |
| logLik.ddf |
log-likelihood value for a fitted detection function |
| logLik.ds |
log-likelihood value for a fitted detection function |
| logLik.io |
log-likelihood value for a fitted detection function |
| logLik.io.fi |
log-likelihood value for a fitted detection function |
| logLik.rem |
log-likelihood value for a fitted detection function |
| logLik.rem.fi |
log-likelihood value for a fitted detection function |
| logLik.trial |
log-likelihood value for a fitted detection function |
| logLik.trial.fi |
log-likelihood value for a fitted detection function |
| p.det |
Double-platform detection probability |
| p.dist.table |
Distribution of probabilities of detection |
| parse.optimx |
Parse optimx results and present a nice object |
| pdot.dsr.integrate.logistic |
Compute probability that a object was detected by at least one observer |
| plot.det.tables |
Observation detection tables |
| plot.ds |
Plot fit of detection functions and histograms of data from distance sampling model |
| plot.io |
Plot fit of detection functions and histograms of data from distance sampling independent observer ('io') model |
| plot.io.fi |
Plot fit of detection functions and histograms of data from distance sampling independent observer model with full independence ('io.fi') |
| plot.layout |
Layout for plot methods in mrds |
| 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 removal distance sampling model |
| plot.trial |
Plot fit of detection functions and histograms of data from distance sampling trial observer model |
| plot.trial.fi |
Plot fit of detection functions and histograms of data from distance sampling trial observer model |
| plot_cond |
Plot conditional detection function from distance sampling model |
| plot_uncond |
Plot unconditional detection function from distance sampling model |
| predict |
Predictions from 'mrds' models |
| predict.ddf |
Predictions from 'mrds' models |
| predict.ds |
Predictions from 'mrds' models |
| predict.io |
Predictions from 'mrds' models |
| predict.io.fi |
Predictions from 'mrds' models |
| predict.rem |
Predictions from 'mrds' models |
| predict.rem.fi |
Predictions from 'mrds' models |
| predict.trial |
Predictions from 'mrds' models |
| predict.trial.fi |
Predictions from 'mrds' models |
| print.ddf |
Simple pretty printer for distance sampling analyses |
| print.ddf.gof |
Prints results of goodness of fit tests for detection functions |
| print.det.tables |
Print results of observer detection tables |
| print.dht |
Prints density and abundance estimates |
| print.p_dist_table |
Print distribution of probabilities of detection |
| 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 |
Print summary of distance detection function model object |
| prob.deriv |
Derivatives for variance of average p and average p(0) variance |
| prob.se |
Average p and average p(0) variance |
| process.data |
Process data for fitting distance sampling detection function |
| pronghorn |
Pronghorn aerial survey data from Wyoming |
| ptdata.distance |
Single observer point count data example from Distance |
| ptdata.dual |
Simulated dual observer point count data |
| ptdata.removal |
Simulated removal observer point count data |
| ptdata.single |
Simulated single observer point count data |
| p_dist_table |
Distribution of probabilities of detection |
| sample_ddf |
Generate data from a fitted detection function and refit the model |
| setbounds |
Set parameter bounds |
| setcov |
Creates design matrix for covariates in detection function |
| sethazard |
Set initial values for detection function based on distance sampling |
| setinitial.ds |
Set initial values for detection function based on distance sampling |
| sim.mix |
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. |
| solvecov |
Invert of covariance matrices |
| stake77 |
Wooden stake data from 1977 survey |
| stake78 |
Wooden stake data from 1978 survey |
| 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 |
Summary of distance detection function model object |
| survey.region.dht |
Extrapolate Horvitz-Thompson abundance estimates to entire surveyed region |