README.md

In PaulMTeggin/practechniques: Functions for the Practical Actuarial Techniques online book

practechniques - Practical Actuarial Techniques

A companion R package for the online book Practical Actuarial Techniques (in development).

THIS IS A PRE-RELEASE VERSION AND NO RELIANCE WHATSOEVER MAY BE PLACED ON IT.

At the time of writing (May 2022) the main feature is a framework using Monte Carlo simulation to calculate p-values for statistical goodness-of-fit tests where the parameters have been estimated from the data.

This is a generalisation of the approach in the KScorrect package and has been tested against results from KScorrect.

The approach has been generalised in three ways:

• General distributions are supported, rather than the closed list used by KScorrect.
• Multiple statistical tests are supported, not just KS.
• Testing can be performed against distributions fitted to overlapping data, not just IID data, using the idea of a Gaussian copula to induce autocorrelation consistent with overlapping data suggested in section 4.2 of the 2019 paper by the Extreme Events Working Party of the UK Institute and Faculty of Actuaries.

With careful configuration, the framework here can in principle also be used where parameters are known in advance rather than estimated from the data, but there is very little value to this use case, as Monte Carlo is rarely necessary when the parameters are known (and is certainly not necessary for the KS and AD tests).

Installation

There is as yet no official release. The code in this repository is the development version, which can be installed from github using the remotes package:

require(remotes)
remotes::install_github('PaulMTeggin/practechniques')

If you don't have remotes installed you should first run

install.packages('remotes')

Bug reports

Users of practechniques are encouraged to report bugs here (go to issues in the menu above, and press new issue to start a new bug report or feature request).

PaulMTeggin/practechniques documentation built on Aug. 19, 2023, 4:44 p.m.