utilityFunctionTools (version 1.0)

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) .

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Version

Install

install.packages('utilityFunctionTools')

Monthly Downloads

211

Version

1.0

License

GPL-3

Maintainer

Sebastian Schneider

Last Published

August 20th, 2025

Functions in utilityFunctionTools (1.0)

compute_function

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

compute_function_aux

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

compute_higher_order_risk_preferences

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

evaluate_cross_validation

Evaluates the cross validation function.

derivative

Computes the derivative of a function

estimate_model

Estimates the model

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).

compute_measures

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

find_optimal_lambda

Finds an optimal penalty weight lambda given the parameters

compute_measures_aux

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

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)