Unicode Extensions for the 'rebus' Package
Build regular expressions piece by piece using human readable code.
This package contains Unicode functionality, and is primarily intended to be
used by package developers.

'imports.R' 'unicode-classes.R' 'unicode-constants.R'
'unicode-general-category-classes.R'
'unicode-general-category-constants.R' 'unicode-operators.R'

Access U.S. Federal Government Recall Data
recalls provides access to U.S. Federal Government recall data on drugs and food from the Food and Drug Administration, car safety from the National Highway Traffic Safety Administration, and product safety recalls from the Consumer Product Safety Commission.

person("Matt", "Tyers", email = "matt.tyers@alaska.gov", role = c("aut", "cre"))
Tools are provided for estimating, testing, and simulating
abundance in a two-event (Petersen) mark-recapture experiment. Functions are
given to calculate the Petersen, Chapman, and Bailey estimators and associated
variances. However, the principal utility is a set of functions to simulate
random draws from these estimators, and use these to conduct hypothesis
tests and power calculations. Additionally, a set of functions are provided
for generating confidence intervals via bootstrapping. Functions are also
provided to test abundance estimator consistency under complete or partial
stratification, and to calculate stratified or partially stratified estimators.
Functions are also provided to calculate recommended sample sizes.
Referenced methods can be found in Arnason et al. (1996) <ISSN:0706-6457>,
Bailey (1951) <DOI:10.2307/2332575>,
Bailey (1952) <DOI:10.2307/1913>,
Chapman (1951) NAID:20001644490,
Cohen (1988) ISBN:0-12-179060-6,
Darroch (1961) <DOI:10.2307/2332748>, and
Robson and Regier (1964) <ISSN:1548-8659>.

person("Matt", "Tyers", email = "matttyersstat@gmail.com", role = c("aut", "cre"))

Simulation of Rare Events Case-Control Studies.
All this package needs is some idea of a risk model and a
population description and it will generate a random
case-control-study from that risk model and population for you.
You may repeat this to simulate powers of testing models and
such.