Implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828> (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) <doi:10.2307/3315735> and Lockhart, Spinelli and Stephens (2007) <doi:10.1002/cjs.5550350111> (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) <doi:10.2307/3316040>. Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values.

It is devoted to Cramer-von Mises goodness-of-fit tests.
    It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.

Implementation of cross-validation method for testing the forecasting accuracy of several multi-population mortality models. The family of multi-population includes several multi-population mortality models proposed through the actuarial and demography literature. The package includes functions for fitting and forecast the mortality rates of several populations. Additionally, we include functions for testing the forecasting accuracy of different multi-population models.
  References, <https://journal.r-project.org/articles/RJ-2025-018/>.
  Atance, D., Debon, A., and Navarro, E. (2020) <doi:10.3390/math8091550>.
  Bergmeir, C. & Benitez, J.M. (2012) <doi:10.1016/j.ins.2011.12.028>.
  Debon, A., Montes, F., & Martinez-Ruiz, F. (2011) <doi:10.1007/s13385-011-0043-z>.
  Lee, R.D. & Carter, L.R. (1992) <doi:10.1080/01621459.1992.10475265>.
  Russolillo, M., Giordano, G., & Haberman, S. (2011) <doi:10.1080/03461231003611933>.
  Santolino, M. (2023) <doi:10.3390/risks11100170>.

Cross-validate one or multiple regression and classification models
    and get relevant evaluation metrics in a tidy format. Validate the
    best model on a test set and compare it to a baseline evaluation.
    Alternatively, evaluate predictions from an external model. Currently
    supports regression and classification (binary and multiclass).
    Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., 
    & Wambugu M. (2019, ISBN: 9781838550134).