Randomization Test for Sequences of Nominal Values
c(
person(given = "Peter", family = "Szabo", role = c("aut", "cre"), email = "peterszabomail77@gmail.com"),
person(given = "Andras", family = "Szilagyi", role = "aut"),
person(given = "Janos", family = "Podani", role = "aut"))
Implements the nomord_probe() function, which performs a statistical analysis on an input vector (sequence) of nominal (categorical) values.
Peter Szabo [aut, cre],
Andras Szilagyi [aut],
Janos Podani [aut]

ROC-type analysis for non-binary gold standards
Estimate and compare the accuracies of diagnostic tests in
situations where the gold standard is continuous, ordinal or
nominal.

U.S. Census Regional and Demographic Data
A collection of various regional information determined by the
U.S. Census Bureau along with demographic data.

Causal Inference in the Presence of Treatment Noncompliance
Under the Binary Instrumental Variable Model
c(
person("Wen Wei", "Loh", email = "wloh@uw.edu", role = c("aut", "cre")),
person("Thomas", "Richardson", role = "aut"))
A finite-population significance test
of the 'sharp' causal null hypothesis that
treatment exposure X has no effect on final outcome Y,
within the principal stratum of Compliers.
A generalized likelihood ratio test statistic is used,
and the resulting p-value is exact.
Currently, it is assumed that there are only Compliers and Never Takers
in the population.
Wen Wei Loh [aut, cre],
Thomas Richardson [aut]

c(
person("Wen Wei", "Loh", email = "wloh@uw.edu", role = c("aut", "cre")),
person("Thomas S.", "Richardson", role = "aut"))
A finite-population significance test of the 'sharp' causal null hypothesis that
treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers.
A generalized likelihood ratio test statistic is used, and the resulting p-value is exact.
Currently, it is assumed that there are only Compliers and Never Takers in the population.
Wen Wei Loh [aut, cre],
Thomas S. Richardson [aut]

Bayesian Analysis of Randomized Experiments with Non-Compliance
person("Scott", "Coggeshall", email = "sscogges@uw.edu", role = c("aut", "cre"))
Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.