person(given = "Rui", family = "Tuo", role = c("ctb","cph"), email = "ruituo@tamu.edu"),
person(given = "Effi", family = "Latiffianti", role = c("ctb","cph"), email = "latiffianti@tamu.edu"))
performing power curve function comparison, covariate matching, and energy decomposition.
deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>,
Packaged: 2022-07-07 15:44:41 UTC; abhinavprakash

Packaged: 2022-07-18 03:19:26 UTC; abhinavprakash

person(given = "Abhinav", family = "Prakash", role = c("aut"), email = "abhinavp@aggienetwork.com"),
Packaged: 2022-09-11 19:06:48 UTC; abhinavprakash

Title: Spatial modelling for distance sampling data
Author: Devin Johnson, Jeff Laake, Jay VerHoef
Description: Provides functions for fitting spatial models to line
transect sampling data and to estimate abundance within a
Depends: R (>= 2.0.0), spatstat, RandomFields, gpclib, mgcv

Depends: R (>= 2.0.0), spatstat (>= 1.22-0), RandomFields, mgcv

Title: Spatial Modelling for Distance Sampling Data
Description: Fits inhomogeneous Poisson process spatial models
to line transect sampling data and provides estimate of
Depends: R (>= 2.0.0), spatstat (>= 1.22-0), RandomFields

Title: Get 'Spotify' API Multiple Information
Description: You can retrieve 'Spotify' API Information such as artists, albums, tracks, features tracks, recommendations or related artists.
This package allows you to search all the information by name and also includes a distance based algorithm to find similar songs.
Packaged: 2020-01-13 13:29:18 UTC; albgonzal

Depends: shapefiles, splancs, mrds, mgcv, methods
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 pre-generated using Distance software) 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