Imports: equate, lavaan, mirt, plink, psych, methods, mvnfast
kableExtra, testthat (>= 2.1.0), rmarkdown, dplyr, pkgdown

Title: Bayesian Prior and Posterior Predictive Replication Assessment
Description: Utilize the Bayesian prior and posterior predictive checking
approach to provide a statistical assessment of replication success
and failure. The package is based on the methods proposed in

Title: Precision-Recall and ROC Curves for Weighted and Unweighted Data
Description: Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method.

Description: Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.

Authors@R: c(person("Song", "Zhai", role = c("aut", "cre"), email =
Description: Construct pharmacogenomics (PGx) polygenic risk score (PRS) with PRS-PGx-Unadj (unadjusted), PRS-PGx-CT (clumping and thresholding), PRS-PGx-L, -GL, -SGL (penalized regression), PRS-PGx-Bayes (Bayesian regression). Package is based on ''Pharmacogenomics Polyenic Risk Score for Drug Response Prediction Using PRS-PGx Methods'' by Zhai, S., Zhang, H., Mehrotra, D.V., and Shen, J., 2021 (submitted).
0.9.37), mvtnorm (>= 1.1.0), propagate (>= 1.0.6), bigparallelr

Title: Stochastic Simulation of Streamflow Time Series using Phase
Authors@R: c(person("Manuela", "Brunner", role = c("aut", "cre"),
person("Reinhard", "Furrer", role = c("aut"),
Author: Manuela Brunner [aut, cre] (<https://orcid.org/0000-0001-8824-877X>),
Description: Provides a simulation framework to simulate streamflow time series with similar
main characteristics as observed data. These characteristics include the distribution of daily
streamflow values and their temporal correlation as expressed by short- and long-range
dependence. The approach is based on the randomization of the phases of the Fourier
transform. We further use the flexible four-parameter Kappa distribution, which allows
for the extrapolation to yet unobserved low and high flows.