Provides tools for sparse regression modelling with grouped predictors using the group 
    subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver 
    near optimal estimates. The group subset penalty can be combined with a group lasso or ridge 
    penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping 
    groups.

Design of group sequential trials, including non-binding futility analysis
  at multiple time points (Gallo, Mao, and Shih, 2014, <doi:10.1080/10543406.2014.932285>).

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 "unique" 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.
    An R Shiny application is available on GitHub.

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>).