A convenient 'R' wrapper to the 'Comet' API, which is a cloud
    platform allowing you to track, compare, explain and optimize machine
    learning experiments and models. Experiments can be viewed on the 'Comet'
    online dashboard at <https://www.comet.com>.

Covariance measure tests for conditional independence testing
    against conditional covariance and nonlinear conditional mean alternatives.
    The package implements versions of the generalised covariance measure test
    (Shah and Peters, 2020, <doi:10.1214/19-aos1857>) and projected covariance
    measure test (Lundborg et al., 2023, <doi:10.1214/24-AOS2447>). The
    tram-GCM test, for censored responses, is implemented including the Cox
    model and survival forests (Kook et al., 2024,
    <doi:10.1080/01621459.2024.2395588>). Application examples to variable
    significance testing and modality selection can be found in Kook and
    Lundborg (2024, <doi:10.1093/bib/bbae475>).

Calculation of various common and less common comfort indices such as predicted mean vote or the two node model. Converts physical variables such as relative to absolute humidity and evaluates the performance of comfort indices.

A set of common functions to be used for displaying messages, checking variables, 
  finding absolute paths, starting applications, etc. More functions will be added later.

Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.

Posterior inference under the convex mixture regression (CoMiRe) models introduced by Canale, Durante, and Dunson (2018) <doi:10.1111/biom.12917>.

Bayesian fit of a Dirichlet Process Mixture with hierarchical multivariate skew normal kernels and coarsened posteriors. For more information, see Gorsky, Chan and Ma (2020) <arXiv:2001.06451>.