Calculates relevance and significance values for
  simple models and for many types of regression models.
  These are introduced in
  'Stahel, Werner A.' (2021)
  "Measuring Significance and Relevance instead of p-values."
  <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>.
  These notions are also applied to replication studies,
  as described in the manuscript
  'Stahel, Werner A.' (2022)
  "'Replicability': Terminology, Measuring Success, and Strategy"
  available in the documentation.

Tools to fit and simulate realizations from relational event models.

A method for fitting the entire regularization path of the
    reluctant generalized additive model (RGAM) for linear regression, logistic, 
    Poisson and Cox regression models. See Tay, J. K., and Tibshirani, R., (2019)
    <arXiv:1912.01808> for details.

Checking the reliability of predictions via the CORP approach,
    which generates provably statistically 'C'onsistent, 'O'ptimally binned, and
    'R'eproducible reliability diagrams using the 'P'ool-adjacent-violators
    algorithm. See Dimitriadis, Gneiting, Jordan (2021) <doi:10.1073/pnas.2016191118>.

Perform structural reliability analysis, including computation and
    simulation with system signatures, Samaniego (2007)
    <doi:10.1007/978-0-387-71797-5>, and survival signatures, Coolen and
    Coolen-Maturi (2013) <doi:10.1007/978-3-642-30662-4_8>. Additionally
    supports parametric and topological inference given system lifetime data,
    Aslett (2012) <https://www.louisaslett.com/PhD_Thesis.pdf>.

Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.

Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.