An easy framework to read FDA Adverse Event Reporting System XML/ASCII files.

Provides a comprehensive Shiny-based graphical user interface
    for conducting a wide range of factor analysis procedures. 'FAfA'
    (Factor Analysis for All) guides users through data uploading,
    assumption checking (descriptives, collinearity, multivariate
    normality, outliers), data wrangling (variable exclusion, data
    splitting), factor retention analysis (e.g., Parallel Analysis, Hull
    method, EGA), Exploratory Factor Analysis (EFA) with various rotation
    and extraction methods, Confirmatory Factor Analysis (CFA) for model
    testing, Reliability Analysis (e.g., Cronbach's Alpha, McDonald's
    Omega), Measurement Invariance testing across groups, and item
    weighting techniques. The application leverages established R packages
    such as 'lavaan' and 'psych' to perform these analyses, offering an
    accessible platform for researchers and students. Results are
    presented in user-friendly tables and plots, with options for
    downloading outputs. Analysis projects can be saved and restored, and
    reproducible R, HTML, and PDF workflow reports can be generated.

Find optimal decisions rules for guiding progression decisions
    following a pilot trial, assuming a hierarchical recruitment model. Estimate
    the time until the main trial recruits to target, given the recruitment data
    observed in the pilot.

Data and functions for the book "Multivariate Statistical 
              Modelling Based on Generalized Linear Models", first edition, by 
              Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.

More comfortable interface to work with R data or source files
    in a key-value fashion.

An implementation of the fair data adaptation with quantile
    preservation described in Plecko & Meinshausen (JMLR 2020, 21(242), 1-44).
    The adaptation procedure uses the specified causal graph to pre-process the
    given training and testing data in such a way to remove the bias caused by
    the protected attribute. The procedure uses tree ensembles for quantile
    regression. Instructions for using the methods are further elaborated in 
    the corresponding JSS manuscript, see <doi:10.18637/jss.v110.i04>.