utilityFunctionTools (version 0.1.1)

P-Spline Regression for Utility Functions and Derived Measures

Description

Predicts a smooth and continuous (individual) utility function from utility points, and computes measures of intensity for risk and higher order risk measures (or any other measure computed with user-written function) based on this utility function and its derivatives according to the method introduced in Schneider (2017) .

Copy Link

Version

Install

install.packages('utilityFunctionTools')

Monthly Downloads

172

Version

0.1.1

License

GPL-3

Maintainer

Sebastian Schneider

Last Published

March 29th, 2021

Functions in utilityFunctionTools (0.1.1)

find_optimal_lambda

Finds an optimal penalty weight lambda given the parameters

compute_function_aux

Computes a continuous and smooth function according to the given utility points

bbase

Constructs a B-spline basis of degree 'deg' (Code by Paul Eilers, Package JOPS, http://statweb.lsu.edu/faculty/marx/JOPS_0.1.0.tar.gz).

derivative

Computes the derivative of a function

evaluate_cross_validation

Evaluates the cross validation function.

compute_function

Computes a continuous and smooth utility function from the given utility points

estimate_model

Estimates the model

tpower

Truncated p-th power function. Helper function for creating the B-Spline basis (Code by Paul Eilers, Package JOPS, http://statweb.lsu.edu/faculty/marx/JOPS_0.1.0.tar.gz)

compute_higher_order_risk_preferences

Computes a continuous and smooth function according to the given utility points

compute_measures

Given a set of smooth and continuous functions, computes predefined and user-defined measures.

compute_measures_aux

Given a set of smooth and continuous functions, computes predefined and user-defined measures.