Title: Vector Binary Tree to Make Your Data Management More Efficient
Description: Provides a new data structure, vector binary tree, to make
your data visiting and management more efficient. if your data has
very structurized column names with specific connecting pattern, it can
read, split, and factorize these names, then build the mapping from all
string objects to an array or tensor, through vector binary tree, by
which the batched data processing can be implemented easily. The methods
of array and tensor are also applicable.

Title: Variational Bayesian mixture models
Description: Variational algorithms and methods for fitting mixture
models. Main functions are varbayes, vbcomp, vbconstr, mppca,
Depends: R (>= 2.10.0), e1071, lattice, grid, nnet, pixmap, mnormt,

Depends: R (>= 2.9.0), e1071, lattice, grid, nnet, pixmap, mnormt, MASS

Title: Variational Bayesian Mixture Models

Title: The Variational Bayesian Method for Sparse PCA
Maintainer: Bo (Yu-Chien) Ning <bo.ning@upmc.fr>
Description: Contains functions for a variational Bayesian method for sparse PCA proposed by Ning (2020) <arXiv:2102.00305>. There are two algorithms: the PX-CAVI algorithm (if assuming the loadings matrix is jointly row-sparse) and the batch PX-CAVI algorithm (if without this assumption). The outputs of the main function, VBsparsePCA(), include the mean and covariance of the loadings matrix, the score functions, the variable selection results, and the estimated variance of the random noise.

Title: Extend the 'copula' Package with Families and Models from
Description: Provides new classes for (rotated) BB1, BB6, BB7, BB8, and
Tawn copulas, extends the existing Gumbel and Clayton families with
rotations, and allows to set up a vine copula model using the 'copula' API.
Corresponding objects from the 'VineCopula' API can easily be converted.
Suggests: lattice, testthat (>= 2.1.0), spelling

Description: ANOVA-type estimation (prediction) of random effects and variance components in linear mixed models,
is implemented. Random models, a sub-set of mixed models, can be fit applying a Variance Component Analysis (VCA).
This is a special type of analysis frequently used in verifying the precision performance of diagnostics.
The Satterthwaite approximation of the total degrees of freedom is implemented. There are several functions
for extracting, random effects, fixed effects, variance-covariance matrices of random and fixed effects.
Residuals can be extracted as raw, standardized and studentized residuals. Additionally, a variability chart
is implemented for visualizing the variability in sub-classes emerging from an experimental design ('varPlot').