Skip to main content

Generalized Lambda Distribution in CSW Parametrization for Python

Project description

gldcswpy is a Python package that implements tools for using Generalized Lambda Distribution (GLD) in CSW Parametrization for Python.

The Generalized Lambda Distribution (GλD) is a flexible family of probability distributions that can assume a wide range of shapes, making it a valuable tool in statistical modeling. It specified by four parameters which determine location, scale and shape of the distribution.

Chalabi et al (2012) introduced a new parameterization of GLD, referred to as CSW Parameterization, wherein the location and scale parameters are directly expressed as the median and interquartile range of the distribution. The two remaining shape parameters characterizing the asymmetry and steepness of the distribution are calculated numerically.

This tool implements the CSW parameterization types of GLD, introduced by Chalabi, Y., Scott, D.J., & Wuertz, D. 2012. It provides methods for calculating parameters of theoretical GLD based on empirical data, generating random sample, estimate Quantile based risk measures such as VaR, ES and so on.

Installation:

pip install gldcswpy

Usage:

https://github.com/KavyaAnnapareddy/using_gldcswpy

References:

  1. Chalabi, Y., Scott, D.J., & Wuertz, D. 2012. Flexible distribution modeling with the generalized lambda distribution.
  2. Freimer, M., Kollia, G., Mudholkar, G.S., & Lin, C.T. 1988. A study of the generalized Tukey lambda family. Communications in Statistics-Theory and Methods, 17, 3547–3567.
  3. S. Su. A discretized approach to flexibly fit generalized lambda distributions to data. Journal of Modern Applied Statistical Methods, 4(2):408–424, 2005.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gldcswpy-0.2.6.tar.gz (12.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gldcswpy-0.2.6-py3-none-any.whl (12.7 kB view details)

Uploaded Python 3

File details

Details for the file gldcswpy-0.2.6.tar.gz.

File metadata

  • Download URL: gldcswpy-0.2.6.tar.gz
  • Upload date:
  • Size: 12.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.5 CPython/3.12.5 Windows/10

File hashes

Hashes for gldcswpy-0.2.6.tar.gz
Algorithm Hash digest
SHA256 895bae1c9e3d96ee3978432df755ec5c068a6d9fc75cc65b8ca9a3a4c5c2ace3
MD5 2f3abc52dbeb13c3792fc4cee3bc6f40
BLAKE2b-256 e01bdafc27df9d6bc7aa324acc8b3d3627fb281f52afedc325c2c46a40e49339

See more details on using hashes here.

File details

Details for the file gldcswpy-0.2.6-py3-none-any.whl.

File metadata

  • Download URL: gldcswpy-0.2.6-py3-none-any.whl
  • Upload date:
  • Size: 12.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.5 CPython/3.12.5 Windows/10

File hashes

Hashes for gldcswpy-0.2.6-py3-none-any.whl
Algorithm Hash digest
SHA256 329358db8a85b093784aa5ae4991a955599c90277d14b2440a803397614022d0
MD5 e88aac9f195695093ed0db0a4391cb9d
BLAKE2b-256 c1bb53a4aeb87874a83e31d09db705e2ef6c3478d5c996ab5460a1f93df75abb

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page