Implementation of conceptual properties norming studies, including estimates of CPNs parameters with their corresponding variances and estimates for the sampling process, and a sampling property function based on a modified empirical distribution from the original data.

Composition of Probabilistic Preferences (CPP)
Luiz O. Gavião, Annibal P. Sant'Anna, Gilson B.A. Lima and Pauli A.A. Garcia.
CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.

Nonparametric Change Point Estimation for Survival Data
Stefanie Krügel [aut, cre],
Alessandra R. Brazzale,  [aut],
Helmut Kuechenhoff [aut]
Nonparametric change point estimation for survival data based on p-values of exact binomial tests.
'CPsurv-package.R' 'bootbiascorrect.R' 'cpest.R'
'cpsurv-data.R' 'cpsurv.R' 'km.sim.survtimes.R' 'methods.R'
'neg.loglik.WeibExp.R' 'sim.survdata.R'

Power Calculation for Weighted Log-Rank Tests in Cure Rate
Models
Emil A. Cornea, Bahjat F. Qaqish, and Joseph G. Ibrahim
This package contains R-functions to perform power
calculation in a group sequential clinical trial with censored
survival data and possibly unequal patient allocation between
treatment and control groups. The fuctions can also be used to
determine the study duration in a clinical trial with censored
survival data as the sum of the accrual duration, which
determines the sample size in a traditional sense, and the
follow-up duration, which more or less controls the number of
events to be observed. This package also contains R functions
and methods to display the computed results.

Compute Cluster Robust Standard Errors with Degrees of Freedom
Adjustments
c(
person("Francis", "Huang",
role = c("aut", "cre"),
email = "flhuang2000@yahoo.com",
comment = c(ORCID = "0000-0002-5900-7763")),
person("Bixi", "Zhang",
role = c("aut"),
email = "bixizhang@missouri.edu")
)
Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on 'Liang and Zeger' (1986) <doi:10.1093/biomet/73.1.13> and Bell and 'McCaffrey' <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ>. Functions used in Huang and Li <doi:10.3758/s13428-021-01627-0> and Huang, 'Wiedermann', and 'Zhang' (forthcoming: Multivariate Behavioral Research).
Francis Huang [aut, cre] (<https://orcid.org/0000-0002-5900-7763>),
Bixi Zhang [aut]