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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()
This function derives the gradients of the negative log likelihood function, with respect to all parameters. It is based on the theory presented in Introduction to Distance Sampling (2001) and Distance Sampling: Methods and Applications (2015). It is not meant to be called by users of the mrds and Distance packages directly but rather by the gradient-based solver. This solver is use when our distance sampling model is for single-observer data coming from either line or point transect and only when the detection function contains an adjustment series but no covariates. It is implement for the following key + adjustment series combinations for the detections function: the key function can be half-normal, hazard-rate or uniform, and the adjustment series can be cosine, simple polynomial or Hermite polynomial. Data can be either binned or exact, but a combination of the two has not been implemented yet.
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