Package index • mrds

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All functions

add.df.covar.line() 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

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.cond(): Average conditional detection function line for plotting

average.line(): Average detection function line for plotting

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

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>) coef(<io>) coef(<io.fi>) coef(<trial>) coef(<trial.fi>) coef(<rem>) coef(<rem.fi>): 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

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

ddf(<ds>): CDS/MCDS Distance Detection Function Fitting

ddf.gof(): Goodness of fit tests for distance sampling models

ddf(<io.fi>): Mark-Recapture Distance Sampling (MRDS) IO - FI

ddf(<io>): Mark-Recapture Distance Sampling (MRDS) IO - PI

ddf(): Distance Detection Function Fitting

ddf(<rem.fi>): Mark-Recapture Distance Sampling (MRDS) Removal - FI

ddf(<rem>): Mark-Recapture Distance Sampling (MRDS) Removal - PI

ddf(<trial.fi>): Mark-Recapture Analysis of Trial Configuration - FI

ddf(<trial>): Mark-Recapture Distance Sampling (MRDS) Trial Configuration - PI

DeltaMethod(): Numeric Delta Method approximation for the variance-covariance matrix

det.tables(): Observation detection tables

detfct.fit.opt(): Fit detection function using key-adjustment functions

detfct.fit(): Fit detection function using key-adjustment functions

dht.deriv(): Computes abundance estimates at specified parameter values using Horvitz-Thompson-like estimator

dht(): Density and abundance estimates and variances

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

flnl.constr.grad.neg(): (Negative) gradients of constraint function

flnl.grad(): Gradient of the negative log likelihood function

flnl(): Log-likelihood computation for distance sampling data

flt.var(): Hessian computation for fitted distance detection function model parameters

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

gstdint(): Integral of pdf of distances

histline(): Plot histogram line

integratedetfct.logistic(): Integrate a logistic detection function

integratelogistic.analytic(): Analytically integrate logistic detection function

integratepdf.grad(): Numerically integrates the non-normalised pdf or the detection function of observed distances over specified ranges.

integratepdf(): Numerically integrate pdf 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?

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

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

mcds(): MCDS function definition

MCDS.exe MCDS mcds_dot_exe: Run MCDS.exe as a backend for mrds

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

mrds_opt: Tips on optimisation issues in mrds models

NCovered(): Compute estimated abundance in covered (sampled) region

nlminb_wrapper(): Wrapper around nlminb

p.det(): Double-platform detection probability

p.dist.table() 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.fi>): Plot fit of detection functions and histograms of data from distance sampling independent observer model with full independence (io.fi)

plot(<io>): Plot fit of detection functions and histograms of data from distance sampling independent observer (io) model

plot(<rem.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(<trial.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_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(<ds>) predict(<io.fi>) predict(<io>) predict(<trial>) predict(<trial.fi>) predict(<rem>) predict(<rem.fi>): Predictions from mrds models

print(<ddf.gof>): Prints results of goodness of fit tests for detection functions

print(<ddf>): Simple pretty printer for distance sampling analyses

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.fi>): Print summary of distance detection function model object

print(<summary.io>): Print summary of distance detection function model object

print(<summary.rem.fi>): Print summary of distance detection function model object

print(<summary.rem>): Print summary of distance detection function model object

print(<summary.trial.fi>): Print summary of distance detection function model object

print(<summary.trial>): 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

qqplot.ddf(): Quantile-quantile plot and goodness of fit tests for detection functions

rem.glm(): Iterative offset model fitting of mark-recapture with removal model

rescale_pars(): Calculate the parameter rescaling for parameters associated with covariates

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

setinitial.ds() sethazard(): 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.fi>): Summary of distance detection function model object

summary(<io>): Summary of distance detection function model object

summary(<rem.fi>): Summary of distance detection function model object

summary(<rem>): Summary of distance detection function model object

summary(<trial.fi>): Summary of distance detection function model object

summary(<trial>): Summary of distance detection function model object

survey.region.dht(): Extrapolate Horvitz-Thompson abundance estimates to entire surveyed region

test.breaks(): Test validity for histogram breaks(cutpoints)

varn() covn(): Compute empirical variance of encounter rate