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 climate data (precipitation,
    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.

A unified interface for connecting to databases ('SQLite', 'MySQL', 'PostgreSQL').
    Just provide the database name and the package will ask you questions
    to help you configure the connection and setup your credentials.  Once
    database configuration and connection has been set up once, you won't
    have to do it ever again.

Descriptive analysis is essential for publishing medical articles.
  This package provides an easy way to conduct the descriptive analysis.
  1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test.
  2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, 'Tukey' for the numeric variables and 'FDR' for the factor variables.
  3. T test, ANOVA or Fisher test can be forced to apply.
  4. Mean and standard deviation can be forced to display.