A collection of functions that creates graphs with error
             bars for data collected from one-way or higher factorial
             designs.

Create and format tables and APA statistics for
    scientific publication. This includes making a 'Table 1'
    to summarize demographics across groups, correlation tables
    with significance indicated by stars, and extracting formatted
    statistical summarizes from simple tests for in-text notation.
    The package also includes functions for Winsorizing data based
    on a Z-statistic cutoff.

A set of 'Rmarkdown' themes for creating scientific and professional documents. Simple interface with features to ease navigation across the page and sub-pages.

Provides an imputation pipeline for single-cell RNA sequencing data. 
  The 'scISR' method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran et.al. (2022) <DOI:10.1038/s41598-022-06500-4>).

Fast maximum-likelihood phylogeny inference from noisy single-cell data using the 'ScisTree' algorithm by Yufeng Wu (2019) <doi:10.1093/bioinformatics/btz676>. 'scistreer' provides an 'R' interface and improves speed via 'Rcpp' and 'RcppParallel', making the method applicable to massive single-cell datasets (>10,000 cells).

You can use the functions provided by the package to make various statistical tables, 
             such as baseline data tables. Creates 'Table 1', i.e., a description of the baseline patient
             characteristics, which is essential in every medical research.
             Supports both continuous and categorical variables, as well as
             p-values and standardized mean differences.
             This method was described by Mary L McHugh (2013) <doi:10.11613/bm.2013.018>.

Single-cell Interpretable Tensor Decomposition (scITD) employs the 
    Tucker tensor decomposition to extract multicell-type gene expression patterns 
    that vary across donors/individuals. This tool is geared for use with single-cell
    RNA-sequencing datasets consisting of many source donors. The method has a wide
    range of potential applications, including the study of inter-individual variation
    at the population-level, patient sub-grouping/stratification, and the analysis
    of sample-level batch effects. Each "multicellular process" that is extracted 
    consists of (A) a multi cell type gene loadings matrix and (B) a
    corresponding donor scores vector indicating the level at which the corresponding
    loadings matrix is expressed in each donor. Additional methods are implemented
    to aid in selecting an appropriate number of factors and to evaluate stability
    of the decomposition. Additional tools are provided for downstream analysis,
    including integration of gene set enrichment analysis and ligand-receptor analysis. 
    Tucker, L.R. (1966) <doi:10.1007/BF02289464>. Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) <doi:10.1007/s13253-011-0055-9>. Zhou, G., & Cichocki, A. (2012) <doi:10.2478/v10175-012-0051-4>.