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Title: Arbitrary Dependency Mixed Multivariate Bayesian Models
Description: Supports Bayesian models with full and partial (hence
arbitrary) dependencies between random variables. Discrete and continuous
variables are supported, and conditional joint probabilities and probability
densities are estimated using Kernel Density Estimation (KDE). The full
general form, which implements an extension to Bayes' theorem, as well as
the simple form, which is just a Bayesian network, both support regression
through segmentation and KDE and estimation of probability or relative
likelihood of discrete or continuous target random variables. This package
also provides true statistical distance measures based on Bayesian models.
Furthermore, these measures can be facilitated on neighborhood searches,
and to estimate the similarity and distance between data points.
Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053>
Suggests: devtools, testthat, covr, e1071, caret, knitr, rmarkdown,
ggplot2, ggpubr, cowplot, philentropy, Rtsne
Imports: Rdpack, datasets, stats, foreach, parallel, doParallel

Title: Multivariate Measurement Error Correction
Description: Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.

Title: Mixed Multivariate Cumulative Incidence Functions
Description: Fits the mixed cumulative incidence functions model suggested by
<doi:10.1093/biostatistics/kxx072> which decomposes within cluster
dependence of risk and timing. The estimation method supports computation in
parallel using a shared memory C++ implementation. A sandwich estimator of the
covariance matrix is available. Natural cubic splines are used to provide a
flexible model for the cumulative incidence functions.
LinkingTo: Rcpp, RcppArmadillo, testthat, psqn
Suggests: testthat (>= 3.0.0), xml2, mvtnorm, R.rsp, mets