It is an open source insurance claim simulation engine sponsored
    by the Casualty Actuarial Society. It generates individual insurance claims 
	including open claims, reopened claims, incurred but not reported claims 
	and future claims. It also includes claim data fitting functions to help set 
	simulation assumptions. It is useful for claim level reserving analysis.
	Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.

Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple 
    competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms 
    of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the 
    fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival 
    curves for a given covariate profile. This approach accommodates any log-linear hazard function of 
    prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide 
    a plot method for visualizing incidence density via population time plots. Based on the case-base sampling 
    approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, 
    and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.

Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. et al. (2002) <doi:10.1016/S0167-9473(02)00058-0>.