Split Knockoff is a data adaptive variable selection framework for controlling the
(directional) false discovery rate (FDR) in structural sparsity, where variable
selection on linear transformation of parameters is of concern. This proposed scheme
relaxes the linear subspace constraint to its neighborhood, often known as variable
splitting in optimization.
Simulation experiments can be reproduced following the Vignette. We include data
(both .mat and .csv format) and application with our method of Alzheimer's Disease
study in this package.
'Split Knockoffs' is first defined in Cao et al. (2021) <arXiv:2103.16159>.

Anthony Christidis <anthony.christidis@stat.ubc.ca>,
Ezequiel Smucler <ezequiels.90@gmail.com>,
Ruben Zamar <ruben@stat.ubc.ca>
Functions for computing split regularized estimators defined in Christidis, Lakshmanan,
Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that
split the set of covariates into groups. The optimal split of the variables into groups and the
regularized estimation of the regression coefficients are performed by minimizing  an objective
function that encourages sparsity within each group and diversity among them.
The estimated coefficients are then pooled together to form the final fit.

Allows to produce and use classification trees with probability (soft) splits.

Allows to produce and use classification trees with soft (probability) splits,
as described in: Dvořák, J. (2019), <doi:10.1007/s00180-019-00867-1>.

The aim of this package is to provide infrastructure for
sports analysis. Anyway, currently it is a selection of data
sets, functions to fetch sports data, examples, and demos --
with the ambition to develop bit by bit a set of classes to
represent general concepts of sports analysis.

Display Tournament Fixtures using Knock Out and Round Robin
Techniques
Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by 'Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).