Covariance measure tests for conditional independence testing 
    against conditional covariance and nonlinear conditional mean alternatives.
    Contains 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:10.48550/arXiv.2211.02039>). 
    Applications can be found in 
    Kook and Lundborg (2024, <doi:10.48550/arXiv.2402.14416>).

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>.

Provides the "comma-free call" operator: '%(%'. Use it to call a
    function without commas between the arguments. Just replace the '('
    with '%(%' in a function call, supply your arguments as standard R
    expressions enclosed by '{ }', and be free of commas (for that call).

Analysis of network community objects with applications to neuroimaging data. There are two main components to this package. The first is the hierarchical multimodal spinglass (HMS) algorithm, which is a novel community detection algorithm specifically tailored to the unique issues within brain connectivity. The other is a suite of semiparametric kernel machine methods that allow for statistical inference to be performed to test for potential associations between these community structures and an outcome of interest (binary or continuous).