Demonstrated Insensitivity to Bias in 2x2xK Contingency Tables
For an observational study with binary treatment, binary outcome and K strata, implements a d-statistic that uses those strata most insensitive to unmeasured bias in treatment assignment.<doi:10.1093/biomet/asaa032>  The package has one function, dstat2x2xk.

Simplifying Descriptive Frequencies and Statistics
person(c("Ronald", "E."), "Robertson",, "rrobertson@aibrt.org", c("cre", "aut"))
Towards automation of descriptive frequencies and statistics tables.

person(c("Ronald", "E."), "Robertson",, "robertson.ron@husky.neu.edu", c("cre", "aut"))

Cell Type Deconvolution from Gene Expressions
Deconvolving cell types from high-throughput gene profiling data.

Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.
c(person("Gregory", "Hunt", email = "ghunt@wm.edu", role = c("aut", "cre")),
person("Johann", "Gagnon-Bartsch", email = "johanngb@umich.edu", role = c("aut")))
Gregory Hunt [aut, cre],
Johann Gagnon-Bartsch [aut]

Discrete Transmuted Generalized Inverse Weibull Distribution
c(
person("Atchanut", "Rattanalertnusorn", email = "atchanut_r@rmutt.ac.th", role = c("cre","aut")),
person("Sirinapa", "Aryuyuen", email = "sirinapa_a@rmutt.ac.th", role = "aut"))
The Discrete Transmuted Generalized Inverse Weibull (DTGIW) distribution is a new distribution for count data analysis. The DTGIW is discrete distribution based on Atchanut and Sirinapa (2021). <DOI: 10.14456/sjst-psu.2021.149>.
Atchanut Rattanalertnusorn [cre, aut],
Sirinapa Aryuyuen [aut]

Karsten Tabelow <tabelow@wias-berlin.de>
Joerg Polzehl <polzehl@wias-berlin.de>
Diffusion Weighted Imaging is a Magnetic Resonance Imaging
modality, that measures diffusion of water in tissues like the human brain.
The package contains R-functions to process diffusion-weighted data in the
context of the diffusion tensor model (DTI). This includes the calculation
of anisotropy measures and, most important, the implementation of our structural
adaptive smoothing algorithm as described in K. Tabelow, J. Polzehl, V. Spokoiny,
and H.U. Voss, Diffusion Tensor Imaging: Structural Adaptive Smoothing,
Neuroimage 39(4), 1763-1773 (2008).
This package is
Copyright (C) 2005-2008 Weierstrass Institute for
Applied Analysis and Stochastics.