Enables chat completion and text annotation with local and 'OpenAI' <https://openai.com/> language models, supporting batch processing, multiple annotators, and consistent output formats.

We provide functions for identifying the core community phylogeny in any microbiome, drawing phylogenetic Venn diagrams, calculating the core Faith’s PD for a set of communities, and calculating the core UniFrac distance between two sets of communities. All functions rely on construction of a core community phylogeny, which is a phylogeny where branches are defined based on their presence in multiple samples from a single type of habitat. Our package provides two options for constructing the core community phylogeny, a tip-based approach, where the core community phylogeny is identified based on incidence of leaf nodes and a branch-based approach, where the core community phylogeny is identified based on incidence of individual branches. We suggest use of the microViz package.

Provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets
    with groups of variables with different degrees of variance, covariance, and effect size.

A 'shiny' application, which allows you to perform single- and
    multi-omics analyses using your own omics datasets. After the upload 
    of the omics datasets and a metadata file, single-omics is performed
    for feature selection and dataset reduction. These datasets are used 
    for pairwise- and multi-omics analyses, where automatic tuning is 
    done to identify correlations between the datasets - the end goal 
    of the recommended 'Holomics' workflow.
    Methods used in the package were implemented in the package 'mixomics'
    by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752>
    and are described there in further detail.

Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.