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.

Principal curves generalize the notion of a first principal component to the case in which it is a nonlinear smooth curve. This package provides a function pcop(X) to compute principal curves with the algorithm defined in Delicado (2001) <doi:10.1006/jmva.2000.1917> from a data matrix X.

Generate a Core Collection with Principal Component Scoring 
    Strategy (PCSS) using qualitative and/or quantitative trait data according 
    to Hamon and Noirot (1990) <https://www.documentation.ird.fr/hor/fdi:36506>, 
    Noirot et al. (1996) <doi:10.2307/2527837> and Noirot et al. (2003) 
    <https://www.documentation.ird.fr/hor/fdi:010031886>.

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

Provides an R interface to the 'RCSB' Protein Data Bank ('PDB') Search
 and Data APIs (<https://www.rcsb.org/>). Supports full-text, attribute,
 sequence, motif, structure, and chemical searches; retrieval of entry-,
 assembly-, polymer-entity-, and chemical-component-level metadata; and
 conversion of API responses into analysis-ready tables and typed R objects for
 reproducible structural bioinformatics workflows.

Performs multinomial goodness-of-fit test on multinomially distributed data using the Randomized phi-divergence test statistics. Details of this kind of statistics can be found at Nikita Puchkin, Vladimir Ulyanov (2023) <doi:10.1214/22-AIHP1299>.