R: Mark-Recapture Distance Sampling

# Mark-Recapture Distance Sampling

## Documentation for package ‘mrds’ version 3.0.1

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

A B C D F G H I K L M N P Q R S T V

mrds-package | Mark-Recapture Distance Sampling (mrds)

## -- A --

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
adj.cos | Cosine adjustment term, not the series.
adj.herm | Hermite polynomial adjustment term, not the series.
adj.poly | Simple polynomial adjustment term, not the series.
adj.series.grad.cos | Series of the gradient of the cosine adjustment series w.r.t. the scaled distance.
adj.series.grad.herm | Series of the gradient of the Hermite polynomial adjustment series w.r.t. the scaled distance.
adj.series.grad.poly | Series of the gradient of the simple polynomial adjustment series w.r.t. the scaled distance.
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

## -- B --

book.tee.data | Golf tee data used in chapter 6 of Advanced Distance Sampling examples

## -- C --

calc.se.Np | Find se of average p and N
cdf.ds | Cumulative distribution function (cdf) for fitted distance sampling detection function
cds | CDS function definition
check.bounds | Check parameters bounds during optimisations
check.mono | Check that a detection function is monotone
coef.ds | Extract coefficients
coef.io | Extract coefficients
coef.io.fi | Extract coefficients
coef.rem | Extract coefficients
coef.rem.fi | Extract coefficients
coef.trial | Extract coefficients
coef.trial.fi | Extract coefficients
coefficients | Extract coefficients
compute.Nht | Horvitz-Thompson estimates 1/p_i or s_i/p_i
covered.region.dht | Covered region estimate of abundance from Horvitz-Thompson-like estimator
covn | Compute empirical variance of encounter rate
create.bins | Create bins from a set of binned distances and a set of cutpoints.
create.command.file | create.command.file
create.model.frame | Create a model frame for ddf fitting
create.varstructure | Creates structures needed to compute abundance and variance

## -- D --

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
distpdf.grad | Gradient of the non-normalised pdf of distances or the detection function for the distances.
ds.function | Distance Sampling Functions

## -- F --

flnl | Log-likelihood computation for distance sampling data
flnl.constr.grad.neg | (Negative) gradients of constraint function
flnl.grad | Gradient of the negative log likelihood function
flpt.lnl | Log-likelihood computation for distance sampling data
flt.var | Hessian computation for fitted distance detection function model parameters

## -- G --

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

## -- H --

histline | Plot histogram line

## -- I --

integratedetfct.logistic | Integrate a logistic detection function
integratelogistic.analytic | Analytically integrate logistic detection function
integratepdf | Numerically integrate pdf of observed distances over specified ranges
integratepdf.grad | Numerically integrates the non-normalised pdf or the detection function of observed distances over specified ranges.
io.glm | Iterative offset GLM/GAM for fitting detection function
is.linear.logistic | Collection of functions for logistic detection functions
is.logistic.constant | Is a logit model constant for all observations?

## -- K --

keyfct.grad.hn | The gradient of the half-normal key function
keyfct.grad.hz | The gradient of the hazard-rate key function
keyfct.th1 | Threshold key function
keyfct.th2 | Threshold key function
keyfct.tpn | Two-part normal key function

## -- L --

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

## -- M --

MCDS | Run MCDS.exe as a backend for mrds
mcds | MCDS function definition
MCDS.exe | Run MCDS.exe as a backend for mrds
mcds_dot_exe | Run MCDS.exe as a backend for mrds
mrds | Mark-Recapture Distance Sampling (mrds)
mrds_opt | Tips on optimisation issues in 'mrds' models

## -- N --

NCovered | Compute estimated abundance in covered (sampled) region
NCovered.ds | Compute estimated abundance in covered (sampled) region
NCovered.io | Compute estimated abundance in covered (sampled) region
NCovered.io.fi | Compute estimated abundance in covered (sampled) region
NCovered.rem | Compute estimated abundance in covered (sampled) region
NCovered.rem.fi | Compute estimated abundance in covered (sampled) region
NCovered.trial | Compute estimated abundance in covered (sampled) region
NCovered.trial.fi | Compute estimated abundance in covered (sampled) region
nlminb_wrapper | Wrapper around 'nlminb'

## -- P --

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.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_layout | Layout for plot methods in mrds
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

## -- Q --

qqplot.ddf | Quantile-quantile plot and goodness of fit tests for detection functions

## -- R --

rem.glm | Iterative offset model fitting of mark-recapture with removal model
rescale_pars | Calculate the parameter rescaling for parameters associated with covariates

## -- S --

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

## -- T --

test.breaks | Test validity for histogram breaks(cutpoints)
two-part-normal | Two-part normal key function

## -- V --

varn | Compute empirical variance of encounter rate