For more complex condition, interaction or restricted cubic spine, TotalPoints.rms()

Title: Randomization Test for Sequences of Nominal Values
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"))
Description: Implements the nomord_probe() function, which performs a statistical analysis on an input vector (sequence) of nominal (categorical) values.

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

Title: U.S. Census Regional and Demographic Data
Author: John A. Ramey <johnramey@gmail.com>
Description: A collection of various regional information determined by the
U.S. Census Bureau along with demographic data.

Title: Causal Inference in the Presence of Treatment Noncompliance
Under the Binary Instrumental Variable Model
person("Wen Wei", "Loh", email = "wloh@uw.edu", role = c("aut", "cre")),
person("Thomas", "Richardson", role = "aut"))
Description: 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,
Currently, it is assumed that there are only Compliers and Never Takers

person("Thomas S.", "Richardson", role = "aut"))
Description: 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.

Title: Bayesian Analysis of Randomized Experiments with Non-Compliance
Authors@R: person("Scott", "Coggeshall", email = "sscogges@uw.edu", role = c("aut", "cre"))
Description: 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>.