c(person(given = "Vathy M.",
family = "Kamulete",
role = c("aut", "cre"),
email = "vathymut@gmail.com",
comment = c(ORCID = "0000-0002-4451-3743")),
person(family = "Royal Bank of Canada (RBC)",
role = "cph",
comment = "Research supported by RBC"))
Test for no adverse shift in two-sample comparison when we
have a training set, the reference distribution, and a test set. The
approach is flexible and relies on a robust and powerful test
statistic, the weighted AUC. Technical details are in Kamulete, V. M.
(2021) <arXiv:1908.04000>. Modern notions of outlyingness such as
trust scores and prediction uncertainty can be used as the underlying
scores for example.
Vathy M. Kamulete [aut, cre] (<https://orcid.org/0000-0002-4451-3743>),
Royal Bank of Canada (RBC) [cph] (Research supported by RBC)

Test for no adverse shift in two-sample comparison when we
have a training set, the reference distribution, and a test set. The
approach is flexible (extensible) and relies on a robust and powerful test
statistic, the weighted AUC. See Kamulete, V. M. (2021) <arXiv:2107.02990>
for details. Outlier scores such as trust scores and prediction uncertainty
can be used as the basis for comparison for example.

Compute Directly Standardized Rates, Ratios and Differences
A set of functions to compute and compare directly standardized rates, rate differences and ratios. A variety of user defined options for analysis (e.g confidence intervals) and formatting are included.

stats, dplyr (>= 0.7.8) , rlang, utils, frailtypack

Tests and Confidence Intervals on Directly Standardized Rates
for Several Methods
Perform a test of a simple null hypothesis about a
directly standardized rate and obtain the matching confidence
interval using a choice of methods.

stats, exactci, asht (>= 0.9.1), loglognorm,

Creates survey designs for distance sampling surveys. These
designs can be assessed for various effort and coverage statistics.
Once the user is satisfied with the design characteristics they can
generate a set of transects to use in their distance sampling survey.
Many of the designs implemented in this R package were first made
available in our 'Distance' for Windows software and are detailed in
Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008,
ISBN-13: 978-0199225873). Find out more about estimating animal/plant
abundance with distance sampling at <http://distancesampling.org/>.
'Class.Constructors.R' 'Coverage.Grid.R' 'Transect.R'
'Region.R' 'generic.functions.R' 'Survey.Design.R'
'Line.Transect.Design.R' 'Line.Transect.R'
'Point.Transect.Design.R' 'Point.Transect.R'
'calc.region.width.R' 'calculate.trackline.pl.R'
'calculate.trackline.zz.R' 'calculate.trackline.zzcom.R'
'check.line.design.R' 'check.point.design.R' 'check.shape.R'
'generate.eqspace.zigzags.R' 'generate.parallel.lines.R'
'generate.random.points.R' 'generate.systematic.points.R'
'get.intersection.points.R' 'run.coverage.R'
'write.transects.R'