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Two-Piece Distributions Implementation

Project description

twopiece: Two Piece Distributions

PyPI version fury.io PyPI license

Overview

The twopiece library provides a Python implementation of the family of Two Piece distributions.

The family of univariate two–piece distributions is a family of univariate three-parameter location-scale models, where skewness is introduced by differing scale parameters either side of the location. For details on this family of distributions we refer to Inference in Two-Piece Location-Scale Models with Jeffreys Priors published in Bayesian Anal. Volume 9, Number 1 (2014), 1-22 and the references therein.

Supported Distributions

Implementation is provided for the following distributions

Three Parameters

  • two-piece normal tpnorm
  • two-piece Laplace tplaplace
  • two-piece Cauchy tpcauchy
  • two-piece logistic tplogistic

Four Parameters

  • two-piece t tpstudent
  • two-piece exponential power tpgennorm

Methods

We provide the following functionality:

  • probability density function pdf
  • cumulative distribution function cdf
  • quantile function ppf
  • random generation random_sample

for all the supported distributions.

Install

We recommend using pip to install twopiece

pip install twopiece

Quick Start

To illustrate usage of the features for the 3 and 4 parameters distributions we will use the two-piece normal, and two-piece t, respectively. The behaviour is analogous for the rest of the supported distributions.

from twopiece.single import *

1. Create a twopiece instance

To create an instance we need to specify either 3 or 4 parameters:

For the two-piece normal we require:

  • loc: which is the location parameter
  • sigma1, sigma2 : which are both scale parameters
loc=0.0
sigma1=1.0
sigma2=1.0
dist = tpnorm(loc=loc, sigma1=sigma1, sigma2=sigma2)

For the two-piece t we require:

  • loc: which is the location parameter
  • sigma1, sigma2 : which are both scale parameters
  • shape : which defines the degrees of freedom for the t-Student distribution
loc=0.0
sigma1=1.0
sigma2=1.0
shape=3.0
dist = tpstudent(loc=loc, sigma1=sigma1, sigma2=sigma2, shape=shape)

Hereafter we assume that there is a twopiece instance called dist.

2. Evaluate and visualise the probability density function (pdf)

We can evaluate the pdf on a single point or an array type object

dist.pdf(0)
dist.pdf([0.0,0.25,0.5])

To visualise the pdf use

x = arange(-12, 12, 0.1)
y = dist.pdf(x)
plt.plot(x, y)
plt.show()

3. Evaluate the cumulative distribution function (cdf)

We can evaluate the cdf on a single point or an array type object

dist.cdf(0)
dist.cdf([0.0,0.25,0.5])

To visualise the cdf use

x = arange(-12, 12, 0.1)
y = dist.cdf(x)
plt.plot(x, y)
plt.show()

4. Evaluate the percent point function (ppf)

We can evaluate the ppf on a single point or an array type object. Note that the ppf has support on [0,1].

dist.ppf(0.95)
dist.ppf([0.5, 0.9, 0.95])

To visualise the ppf use

x = arange(0.001, 0.999, 0.01)
y = dist.ppf(x)
plt.plot(x, y)
plt.show()

5. Generate a random sample

To generate a random sample we require:

  • size: which is simply the size of the sample
sample = dist.random_sample(size = 100)

Requirements

twopiece has been developed and tested on Python 3.6, and 3.7

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