Some R functions, such as optim(), require a function
  its gradient passed as separate arguments. When these are
  expensive to calculate it may be much faster to calculate
  the function (fn) and gradient (gr) together since they often share
  many calculations (chain rule). This package allows the user
  to pass in a single function that returns both the function
  and gradient, then splits (hence 'splitfngr') them
  so the results can be accessed
  separately. The functions provided allow this to be done with
  any number of functions/values, not just for functions and gradients.

Functions to compute split generalized linear models. The approach fits 
             generalized linear models that split the covariates into groups. The 
             optimal split of the variables into groups and the regularized estimation 
             of the coefficients are performed by minimizing an objective function 
             that encourages sparsity within each group and diversity among them.
             Example applications can be found in Christidis et al. (2021)
             <arXiv:2102.08591>.

Estimate the internal consistency of your tasks with a permutation based split-half reliability approach.
    Unofficial release name: "I eat stickers all the time, dude!".

Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, and the option to sub-sample data.

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. 
             'Split Knockoffs' is first defined in Cao et al. (2021) <doi:10.48550/arXiv.2103.16159>.