Fit and simulate any kind of
    physiologically-based kinetic ('PBK') models whatever the number of compartments.
    Moreover, it allows to account for any link between pairs of compartments, as
    well as any link of each of the compartments with the external medium. Such
    generic PBK models have today applications in pharmacology (PBPK models) to
    describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe
    chemical substance effects. In case of exposure to a parent compound (drug or
    chemical) the 'rPBK' package allows to consider metabolites, whatever their number
    and their phase (I, II, ...). Last but not least, package 'rPBK' can also be used for
    dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also
    Charles et al. (2022) <doi:10.1101/2022.04.29.490045>.

Computes the ridge partial correlation
    coefficients in a high or ultra-high dimensional linear regression
    problem. An extended Bayesian information criterion is also
    implemented for variable selection. Users provide the matrix
    of covariates as a usual dense matrix or a sparse matrix
    stored in a compressed sparse column format. Detail of the method
    is given in the manual.

Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.

Provides tools to read, write, visualize Protein Data Bank (PDB) files and
    perform some structural manipulations.