Group SLOPE (Group Sorted L1 Penalized Estimation) is
    a penalized linear regression method that is used for adaptive
    selection of groups of significant predictors in a high-dimensional
    linear model. The Group SLOPE method can control the (group) false
    discovery rate at a user-specified level (i.e., control the expected
    proportion of irrelevant among all selected groups of predictors).
    For additional information about the implemented methods please see
    Brzyski, Gossmann, Su, Bogdan (2018) <doi:10.1080/01621459.2017.1411269>.

Methods include converting series of event names to strings, finding common patterns
    in a group of strings, discovering featured patterns when comparing two groups of strings as well
    as the number and starting position of each pattern in each string, obtaining transition matrix, 
    computing transition entropy, statistically comparing the difference between two groups of strings,
    and clustering string groups. Event names can be any action names or labels such as events in log
    files or areas of interest (AOIs) in eye tracking research.

Alternative implementations of some base R functions, including sort, order, and match.  Functions are simplified but can be faster or have other advantages.

Computational resources for test proposed by Gibbons, Ross, Shanken (1989)<DOI:10.2307/1913625>.
	It also has the functions for the power analysis and the choice of the optimal level of significance.
	The optimal level is determined by minimizing the expected loss from hypothesis testing.

Simulation and analysis of graded response data with different types of estimators. Also, an interactive shiny application is provided with graphics for characteristic and information curves. Samejima (2018) <doi:10.1007/978-1-4757-2691-6_5>.

Statistical testing procedures for detecting
  GxE (gene-environment) interactions. The main focus lies on
  GRSxE interaction tests that aim at detecting GxE interactions
  through GRS (genetic risk scores). Moreover, a novel testing
  procedure based on bagging and OOB (out-of-bag) predictions is
  implemented for incorporating all available observations at
  both GRS construction and GxE testing (Lau et al., 2023,
  <doi:10.1038/s41598-023-28172-4>).