Description: Identifies a bicluster, a submatrix of the data such that the features and observations within the submatrix differ from those not contained in submatrix, using a two-step method. In the first step, observations in the bicluster are  identified to maximize the sum of weighted between cluster feature differences. The method is described in Helgeson et al. (2020) <doi:10.1111/biom.13136>. 'SCBiclust' can be used to identify biclusters which differ based on feature means, feature variances, or more general differences.

Title: Simultaneous Confidence Bands for the Mean of Functional Data
Description: This package implements statistical methods for estimating and inferring the mean of functional data. The methods are based on simultaneous confidence bands and local polynomial fitting. They feature: bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
Depends: R (>= 2.10), stats, graphics, boot, KernSmooth

Description: Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting,  bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.

Packaged: 2016-12-26 22:35:56 UTC; daviddegras

Title: Stochastic Complexity-Based Conditional Independence Test for
Author: Alexander Marx [aut,cre] Jilles Vreeken [aut]
Description: An efficient implementation of SCCI using 'Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data.

Title: The Self-Controlled Case Series Method
Author: Yonas Ghebremichael Weldeselassie, Heather Whitaker, Paddy Farrington
Description: Various self-controlled case series models used to investigate
associations between time-varying exposures such as vaccines or
other drugs or non drug exposures and an adverse event can be
fitted. Detailed information on the self-controlled case series
method and its extensions with more examples can be found in
Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y.
(2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies:
A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press)
Imports: survival, splines, dummies, corpcor, fda, R.methodsS3, gnm