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) |
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 |
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 | 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 |
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 |
flnl | Log-likelihood computation for distance sampling data |
flpt.lnl | 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 |
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 |
histline | Plot histogram line |
integratedetfct.logistic | Integrate a logistic detection function |
integratelogistic.analytic | Analytically integrate logistic detection function |
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.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 |
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 |
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 |
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.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 |
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 |
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 |
test.breaks | Test validity for histogram breaks(cutpoints) |
two-part-normal | Two-part normal key function |
varn | Compute empirical variance of encounter rate |