Provides actuarial modeling tools for Monte Carlo loss simulations, loss reserving, and reinsurance layer loss calculations. It enables users to generate stochastic loss datasets with customisable frequency and severity distributions, fit development patterns to claim triangles, and calculate reinsurance losses for occurrence and aggregate layers with user-defined retentions, limits, and reinstatements. For development pattern selection, the package includes a machine learning approach that evaluates multiple reserving models using holdout validation to identify the best-fitting pattern based on predictive accuracy, this is based on the algorithm described in Richman, R and Balona, C (2020)< https://www.ssrn.com/abstract=3697256>.