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A collection of tools for clinical trial data management and 
    analysis in research and teaching.
    The package is mainly collected for personal use, but any use beyond that is encouraged.
    This package has migrated functions from 'agdamsbo/daDoctoR', and new functions has been added.
    Version follows months and year. See NEWS/Changelog for release notes.
    This package includes sampled data from the TALOS trial (Kraglund et al (2018) <doi:10.1161/STROKEAHA.117.020067>).
    The win_prob() function is based on work by Zou et al (2022) <doi:10.1161/STROKEAHA.121.037744>.
    The age_calc() function is based on work by Becker (2020) <doi:10.18637/jss.v093.i02>.

Stores the data associated with your amplicon sequence analysis. This includes nucleotide sequences, abundance, sample and treatment assignments, taxonomic classifications, asv, otu and phylotype clusters, metadata, trees and various reports. It is designed to facilitate data analysis across multiple R packages with utility functions to read / write from 'mothur', 'qiime2', 'dada2', and 'phyloseq'.

R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.

A comprehensive set of string manipulation functions based on those found in 'Python'
    without relying on 'reticulate'. It provides functions that intend to (1) make it easier for
    users familiar with 'Python' to work with strings, (2) reduce the complexity often associated
    with string operations, (3) and enable users to write more readable and maintainable code
    that manipulates strings.

Testing, monitoring and dating structural changes in (linear)
             regression models. strucchange features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
	     fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
	     recursive/moving estimates) and F statistics, respectively.
             It is possible to monitor incoming data online using
             fluctuation processes.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals.
             Emphasis is always given to methods for visualizing the data.