Implements two differentially private algorithms for
estimating L2-regularized logistic regression coefficients. A randomized
algorithm F is epsilon-differentially private (C. Dwork, Differential
Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if
|log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
for any pair D, D' of datasets that differ in exactly one record, any
measurable set S, and the randomness is taken over the choices F makes.

PRO-CTCAE Data Management, Analysis, and Graphical Tools
Blake Langlais [aut, cre], Amylou Dueck [aut]
A collection of tools to facilitate standardized analysis
and graphical procedures when using the National Cancer Institute’s
Patient-Reported Outcomes version of the Common Terminology Criteria for
Adverse Events (PRO-CTCAE).
ggplot2, ggnewscale, ggtext, dplyr, Hmisc, magrittr

ggplot2, ggnewscale, ggtext, dplyr, Hmisc, magrittr,
ExactCIdiff, DescTools

PRO-CTCAE Scoring, Analysis, and Graphical Tools
Blake Langlais [aut, cre],
Brie Noble [ctb],
Molly Klanderman [ctb],
Amylou Dueck [aut]
ggplot2, ggnewscale, ggtext, dplyr, Hmisc, magrittr,
ggpattern, DescTools, gridExtra

Blake Langlais [aut, cre],
Amylou Dueck [aut],
Molly Klanderman [ctb]
A collection of tools to facilitate standardized analysis
and graphical procedures when using the National Cancer Institute’s
Patient-Reported Outcomes version of the Common Terminology Criteria for
Adverse Events (PRO-CTCAE). NOTE: Some tools here require additional
packages be installed which are not available on CRAN at the time of this
version update ('ProAE' v0.2.8). These packages and their respective
repositories are listed here: 'ggpattern'
<https://github.com/coolbutuseless/ggpattern>.
ggplot2, ggnewscale, ggtext, dplyr, Hmisc, magrittr,
ExactCIdiff, DescTools, gridExtra

Product Density Estimation for ICA using tilted Gaussian density
estimates
A direct and flexible method for estimating an ICA model.
This approach estimates the densities for each component
directly via a tilted gaussian. The tilt functions are
estimated via a GAM poisson model. Details can be found in
"Elements of Statistical Learning (2nd Edition)" Section 14.7.4