Differential Item Functioning (DIF) Analysis with shiny application interfaces. You can run the functions in this package without any arguments and perform your DIF analysis using user-friendly interfaces.

Item focussed recursive partitioning for simultaneous selection of items and variables that induce Differential Item Functioning (DIF) in dichotomous or polytomous items.

Implementation of a function 'digest()' for the creation of hash
 digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32',
 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128'
 algorithms) permitting easy comparison of R language objects, as well as functions
 such as 'hmac()' to create hash-based message authentication code. Please note that
 this package is not meant to be deployed for cryptographic purposes for which more
 comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.

Provides an extensive and curated collection of datasets related to the digestive system, stomach, intestines, liver, pancreas, and associated diseases. 
    This package includes clinical trials, observational studies, experimental datasets, cohort data, and case series involving gastrointestinal disorders such as gastritis, ulcers, pancreatitis, liver cirrhosis, colon cancer, colorectal conditions, Helicobacter pylori infection, irritable bowel syndrome, intestinal infections, and post-surgical outcomes.
    The datasets support educational, clinical, and research applications in gastroenterology, public health, epidemiology, and biomedical sciences. 
    Designed for researchers, clinicians, data scientists, students, and educators interested in digestive diseases, the package facilitates reproducible analysis, modeling, and hypothesis testing using real-world and historical data.

Adopts the general least squares-based data-driven normalization strategy 
  developed by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250> to
  correct for technical variance in gene expression data generated via digital 
  polymerase chain reaction (dPCR). Performs normalization of raw copy numbers
  and also calculates relative variability metrics that can be used to assess the impact 
  of normalization on variance.