Implements regression calibration methods for correcting 
    measurement error in regression models using external or internal 
    reliability studies. Methods are described in Carroll, Ruppert, 
    Stefanski, and Crainiceanu (2006) "Measurement Error in Nonlinear 
    Models: A Modern Perspective" <doi:10.1201/9781420010138>.

Contains basic tools for visualizing, interpreting, and building regression models.  It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9.

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

Provides statistical tools for evaluating how covariates
    influence the strength of Pearson correlation coefficients between
    two response variables. Supports bivariate normal and bivariate
    binary responses, with likelihood-based inference and bootstrap-based
    significance testing. The methodology is based on Dufera, Liu and Xu
    (2023) "Regression models of Pearson correlation coefficient"
    <doi:10.1080/24754269.2023.2164970>.

Regularised discriminant analysis functions. The classical regularised discriminant analysis proposed by Friedman in 1989, including cross-validation, of which the linear and quadratic discriminant analyses are special cases. Further, the regularised maximum likelihood linear discriminant analysis, including cross-validation. References: Friedman J.H. (1989): "Regularized Discriminant Analysis". Journal of the American Statistical Association 84(405): 165--175. <doi:10.2307/2289860>. Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>. Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>.