Computes 46 optimized distance and similarity measures for comparing probability functions. These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

c(person("Hajk-Georg", "Drost",
role = c("aut", "cre"),
email = "hajk-georg.drost@tuebingen.mpg.de",
comment = c(ORCID = "http://orcid.org/0000-0002-1567-306X")),
person("Jakub", "Nowosad", role = c("ctb"), email="nowosad.jakub@gmail.com", comment = c(ORCID = "0000-0002-1057-3721"))
)
Hajk-Georg Drost [aut, cre] (<https://orcid.org/0000-0002-1567-306X>),
Jakub Nowosad [ctb] (<https://orcid.org/0000-0002-1057-3721>)

Download Data from the Philadelphia District Attorney's Office
Provides a series of helper functions to download and
clean the data that is publicly available on the Philadelphia District
Attorney's Office Public Data Dashboard
(<https://data.philadao.com/download.html>). This package is not an
official product of the Philadelphia District Attorney's Office - it is
merely a way to handle their already public and freely available data.

Nonparametric goodness-of-fit methods based on phi-divergences
This package perform a generalized goodness-of-fit test
based on phi-divergences.  This test works by considering the
maximum 'distance' between the hypothesized distribution
function and the empirical distribution function, where the
measure of 'distance' is based on a phi-divergence.  This
generalized family of tests is indexed by a real-valued
parameter, s, wich can take values in [-1,2].  Special cases of
this family include the Berk-Jones statistic (s=0), the
supremum form of the Anderson-Darling statistic (s=2), and the
statistic of Jaescke and Eicker (s=-1).  See the references for
the phi.test() function for references regarding these special
cases.  In addition to performing a statistical test, this
package will also invert the test statistic to form and plot
confidence bands for the true distribution function for sample
sizes up to 10,000.