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.

Comprehensive analytical tools are provided to characterize infectious disease superspreading from contact tracing surveillance data. The underlying theoretical frameworks of this toolkit include branching process with transmission heterogeneity (Lloyd-Smith et al. (2005) <doi:10.1038/nature04153>), case cluster size distribution (Nishiura et al. (2012) <doi:10.1016/j.jtbi.2011.10.039>, Blumberg et al. (2014) <doi:10.1371/journal.ppat.1004452>, and Kucharski and Althaus (2015) <doi:10.2807/1560-7917.ES2015.20.25.21167>), and decomposition of reproduction number (Zhao et al. (2022) <doi:10.1371/journal.pcbi.1010281>).

Automate the explanatory analysis of machine learning predictive 
    models. Generate advanced interactive model explanations in the form of 
    a serverless HTML site with only one line of code. This tool is 
    model-agnostic, therefore compatible with most of the black-box predictive
    models and frameworks. The main function computes various (instance and 
    model-level) explanations and produces a customisable dashboard, which 
    consists of multiple panels for plots with their short descriptions. It is 
    possible to easily save the dashboard and share it with others. modelStudio 
    facilitates the process of Interactive Explanatory Model Analysis introduced 
    in Baniecki et al. (2023) <doi:10.1007/s10618-023-00924-w>.