Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.

A progression model for repeated measures (PMRM)
  is a continuous-time nonlinear mixed-effects model for
  longitudinal clinical trials in progressive diseases.
  Unlike mixed models for repeated measures (MMRMs),
  which estimate treatment effects as linear combinations
  of additive effects on the outcome scale,
  PMRMs characterize treatment effects
  in terms of the underlying disease trajectory.
  This framing yields clinically interpretable quantities
  such as average time saved
  and percent reduction in decline due to treatment.
  This package implements frequentist PMRMs by
  Raket (2022) <doi:10.1002/sim.9581> using
  'RTMB' by Kristensen (2016) <doi:10.18637/jss.v070.i05>.

Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.

Provides analytic and simulation tools to estimate the minimum
    sample size required for achieving a target prediction mean-squared error
    (PMSE) or a specified proportional PMSE reduction (pPMSEr) in linear
    regression models.  Functions implement the criteria of Ma (2023) 
    <https://digital.wpi.edu/downloads/0g354j58c>, support covariance-matrix 
    handling, and include helpers for root-finding and diagnostic plotting.