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>.

Actuarial reports are prepared for the last day of a specific 
    period, such as a month, a quarter or a year. Actuarial models assume that 
    certain events happen at the beginning or end of periods. The package 
    contains functions to easily refer to the first or last (working) day 
    within a specific period relative to a base date to facilitate actuarial 
    reporting and to compare results.

Experience studies are used by actuaries to explore historical
    experience across blocks of business and to inform assumption setting
    activities. This package provides functions for preparing data, creating 
    studies, visualizing results, and beginning assumption development. 
    Experience study methods, including exposure calculations, are described in:
    Atkinson & McGarry (2016) "Experience Study Calculations" 
    <https://www.soa.org/49378a/globalassets/assets/files/research/experience-study-calculations.pdf>.
    The limited fluctuation credibility method used by the 'exp_stats()'
    function is described in: Herzog (1999, ISBN:1-56698-374-6) 
    "Introduction to Credibility Theory".

This code provides a simple method for representing a visual scene as it may be seen by an animal with less acute vision. When using (or for more information), please cite the original publication.