Imports: mgcv, gratia, graphics, stats, copula, sn, Rdpack

Imports: mrds, Distance, sf, sp, ggplot2, mgcv, rgeos, methods,
Suggests: testthat, parallel, pbapply, knitr, lwgeom, rmarkdown
Description: Performs distance sampling simulations. 'dsims' repeatedly generates
instances of a user defined population within a given survey region. It then
generates realisations of a survey design 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 process allows users to optimise
survey designs for their specific set of survey conditions. The effects of
uncertainty in population distribution or parameters can be investigated
under a number of simulations so that users can be confident that they have
achieved a robust survey design before deploying vessels into the field. The
distance sampling designs used in this package from 'dssd' are detailed in
Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13:
978-0199225873). General distance sampling methods are detailed in Introduction
to Distance Sampling: Estimating Abundance of Biological Populations, Buckland
et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating
animal/plant abundance with distance sampling at <http://distancesampling.org/>.
Collate: 'AICc.R' 'generic.functions.R' 'Density.Summary.R' 'Density.R'

person("Laura", "Marshall", email = "lhm@st-and.ac.uk", role = c("aut", "cre")),
person("Thomas", "Len", email = "len.thomas@st-andrews.ac.uk", role = "ctb"))

Description: Datasets and functions that facilitate data analysis labs in data science courses and workshops.
Maintainer: Rafael A. Irizarry <rafa@jimmy.harvard.edu>

Description: Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops.

Description: Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.