The model.matrix() function in R is  convenient for transforming training dataset for modeling. 
    But it does not save any parameter used in transformation, so it is hard to apply the same transformation 
    to test dataset or new dataset. This package is created to solve the problem.

Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.

A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.

Functions for modelling that help you seamlessly integrate
    modelling into a pipeline of data manipulation and visualisation.

A comprehensive package for comparing multiple Structural
    Equation Models (SEM). Supports both nested and non-nested
    model comparisons, chi-square difference tests, and extraction
    of multiple fit indices including AIC (Akaike Information Criterion),
    BIC (Bayesian Information Criterion), CFI (Comparative Fit Index),
    TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation),
    and SRMR (Standardized Root Mean Square Residual). Built on top of the
    'lavaan' package for seamless SEM model comparison workflows. The Vuong test
    (Vuong, 1989) for non-nested models is used as the statistical test.

Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) <doi:10.5281/zenodo.17119597> (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package.