Goodness-of-Fit Tests for Univariate Data via Energy
Create an asymmetric Laplace distribution object for energy testing
Create a Bernoulli distribution object for energy testing
Create a beta distribution object for energy testing
Create a Binomial distribution object for energy testing
Create a Cauchy distribution object for energy testing
Create a Chi-squared distribution object for energy testing
energyGOF: Goodness-of-Fit Tests via the Energy of Data
Goodness-of-fit tests for univariate data via energy
S3 Interface to Parametric Goodness-of-Fit Tests via Energy
Create an Exponential distribution object for energy testing
Create an F distribution object for energy testing
Create a gamma distribution object for energy testing
Create a geometric distribution object for energy testing
Create a half-normal distribution object for energy testing
Create an inverse Gaussian distribution object for energy testing
Create a Laplace distribution object for energy testing
Create a lognormal distribution object for energy testing
Create a Normal distribution object for energy testing
Create a Pareto (type I) distribution object for energy testing
Create a Poisson distribution object for energy testing
Create a Uniform distribution object for energy testing
Create a Weibull distribution object for energy testing
Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap.