Collate: 'generic.functions.R' 'Region.R' 'Density.R' 'Detectability.R'

Depends: graphics, splancs, mrds, mgcv, shapefiles, methods
Collate: 'generic.functions.R' 'Survey.Design.R' 'Region.R' 'Density.R'

Suggests: testthat, parallel, knitr, rmarkdown
be pregenerated using Distance software <http://distancesampling.org/>) and
simulates the detection process. The data are then analysed
so that the results can be compared for accuracy and precision
across all replications. This will allow users to select survey
designs which will give them the best accuracy and precision

be generated using Distance software in advance <http://distancesampling.org/>)
and simulates the detection process. The data are then analysed

Description: Performs distance sampling simulations. It repeatedly generates
instances of a user defined population within a given survey region, generates
realisations of a survey design (currently these must be generated using
Distance software in advance <http://distancesampling.org/>) and simulates
the detection process. The data are then analysed so that the results can
be compared for accuracy and precision across all replications. This will
allow users to select survey designs which will give them the best accuracy
and precision given their expectations about population distribution. Any
uncertainty in population distribution or population parameters can be
included by running the different survey designs for a number of different
population descriptions. An example simulation can be found in the help file for

Imports: graphics, splancs, mgcv, shapefiles, rgeos, sp, fields

Imports: graphics, splancs, mgcv, shapefiles, rgeos, fields, sp
Suggests: testthat, parallel, pbapply, knitr, rmarkdown
Description: Performs distance sampling simulations.It repeatedly generates

Title: Recursive Partitioning for Discrete-Time Survival Trees
Author: Peter Mayer, Denis Larocque, Matthias Schmid
Description: Building discrete-time survival trees and bagged trees based on
the functionalities of the rpart package. Splitting criterion maximizes the
likelihood of a covariate-free logistic discrete time hazard model.