A fast and general method for restructuring classical longitudinal
    observational data into augmented transition data suitable for multi-state
    modeling with the 'msm' package. Works with any longitudinal data where
    subjects accumulate repeated observations with start and end times and an
    optional terminal outcome. Methods are described in Grossetti, Ieva and
    Paganoni (2018) <doi:10.1007/s10729-017-9400-z>.

Provides fundamental functions for descriptive statistics,
    including MODE(), estimate_mode(), center_stats(), position_stats(), pct(),
    spread_stats(), kurt(), skew(), and shape_stats(), which assist in
    summarizing the center, spread, and shape of numeric data. For more details,
    see McCurdy (2025), "Introduction to Data Science with R" 
    <https://jonmccurdy.github.io/Introduction-to-Data-Science/>.

Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). 
             The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler".

Implements a semi-parametric estimation framework combined with a boosting algorithm to marginally estimate the conditional cumulative distribution function of survival times given informative covariates. It then utilizes the graphical lasso method to reconstruct network structures among multivariate time-to-event variables, accommodating both multivariate outcomes measured within a single dataset and survival times integrated from heterogeneous (multi-source) datasets..

Multivariate Analysis methods and data sets used
    in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
    This also serves as a companion package for the 
    STAT 571: Multivariate Analysis course offered by the Department of Statistics
    at the University of Illinois at Urbana-Champaign ('UIUC').

Enable operationalized evaluation of disease outcomes in
    multiple sclerosis. ‘MSoutcomes’ requires longitudinally recorded
    clinical data structured in long format. The package is based on the
    research developed at Clinical Outcomes Research unit (CORe),
    University of Melbourne and Neuroimmunology Centre, Royal Melbourne
    Hospital. Kalincik et al. (2015) <doi:10.1093/brain/awv258>.
    Lorscheider et al.  (2016) <doi:10.1093/brain/aww173>. Sharmin et al.
    (2022) <doi:10.1111/ene.15406>.  Dzau et al. (2023)
    <doi:10.1136/jnnp-2023-331748>.