The encoding of colour can be handled in many different ways,
    using different colour spaces. As different colour spaces have
    different uses, efficient conversion between these representations are
    important. The 'farver' package provides a set of functions that gives
    access to very fast colour space conversion and comparisons
    implemented in C++, and offers speed improvements over the
    'convertColor' function in the 'grDevices' package.

The 'FAS' package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) <https://www.jmlr.org/papers/volume22/20-539/20-539.pdf> and Beyhum & Striaukas (2023) <arXiv:2307.13364>.

An implementation of the two-sample multivariate
    Kolmogorov-Smirnov test described by Fasano and Franceschini (1987)
    <doi:10.1093/mnras/225.1.155>. This test evaluates the null hypothesis
    that two i.i.d. random samples were drawn from the same underlying
    probability distribution. The data can be of any dimension, and can be
    of any type (continuous, discrete, or mixed).

Latent process embedding for functional network data with the Functional Adjacency Spectral Embedding. Fits smooth latent processes based on cubic spline bases. Also generates functional network data from three models, and evaluates a network generalized cross-validation criterion for dimension selection. For more information, see MacDonald, Zhu and Levina (2022+) <arXiv:2210.07491>.

It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.

This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.