Perform analysis of variance and other important complementary
  analyses.  The functions are easy to use.  Performs analysis in various
  designs, with balanced and unbalanced data.

Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. 
    The package supports cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM', 
    as well as network comparison tests fitted using the packages 'bgms' and 'BBGM'. 
    The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the 
    posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior 
    structure space. Furthermore, the package comes with an extensive suite for visualizing results.

Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.

Extracting desired data using the proper Census variable names can 
    be time-consuming. This package takes the pain out of that process by 
    providing functions to quickly locate variables and download labeled tables 
    from the Census APIs (<https://www.census.gov/data/developers/data-sets.html>).

Get high-resolution (1 km) daily, monthly and annual climate data (precipitation,
    and average, minimum and maximum temperatures) for points and polygons within
    Europe.

Univariate and multivariate methods for compositional data 
    analysis, based on logratios. The package implements the approach in the
    book Compositional Data Analysis in Practice by Michael Greenacre (2018),
    where accent is given to simple pairwise logratios. Selection can be made
    of logratios that account for a maximum percentage of logratio variance.
    Various multivariate analyses of logratios are included in the package.