Description:
lmo 0.14.2
Lmo - Trimmed L-moments and L-comoments
Unlike the legacy
product-moments, the
L-moments uniquely describe a
probability distribution, and are more robust and efficient.
The "L" stands for Linear; it is a linear combination of order statistics.
So Lmo is as fast as sorting your samples (in terms of time-complexity).
Key Features
Calculates trimmed L-moments and L-comoments, from samples or any
scipy.stats distribution.
Full support for trimmed L-moment (TL-moments), e.g.
lmo.l_moment(..., trim=(1/137, 3.1416)).
Generalized Method of L-moments: robust distribution fitting that beats MLE.
Fast estimation of L-comoment matrices from your multidimensional data
or multivariate distribution.
Goodness-of-fit test, using L-moment or L-moment ratio's.
Exact (co)variance structure of the sample- and population L-moments.
Theoretical & empirical influence functions of L-moments & L-ratio's.
Complete docs, including detailed API
reference with usage examples and with mathematical TEX definitions.
Clean Pythonic syntax for ease of use.
Vectorized functions for very fast fitting.
Fully typed, tested, and tickled.
Optional Pandas integration.
Quick example
Even if your data is pathological like
Cauchy, and the L-moments
are not defined, the trimmed L-moments (TL-moments) can be used instead.
Let's calculate the TL-location and TL-scale of a small amount of samples:
>>> import numpy as np
>>> import lmo
>>> rng = np.random.default_rng(1980)
>>> x = rng.standard_cauchy(96) # pickle me, Lmo
>>> lmo.l_moment(x, [1, 2], trim=(1, 1)).
array([-0.17937038, 0.68287665])
Now compare with the theoretical standard Cauchy TL-moments:
>>> from scipy.stats import cauchy
>>> cauchy.l_moment([1, 2], trim=(1, 1))
array([0. , 0.69782723])
See the documentation for more examples and
the API reference.
Roadmap
Automatic trim-length selection.
Plotting utilities (deps optional), e.g. for L-moment ratio diagrams.
Installation
Lmo is on PyPI, so you can do something like:
pip install lmo
Dependencies
These are automatically installed by your package manager when installing Lmo.
Package
Supported versions
Python
>=3.10
NumPy
>=1.23
SciPy
>=1.9
Additionally, Lmo supports the following optional packages:
Package
Supported versions
Installation
Pandas
>=1.5
pip install Lmo[pandas]
See SPEC 0 for more information.
Foundational Literature
J.R.M. Hosking (1990) – L-moments: Analysis and Estimation of
Distributions using Linear Combinations of Order Statistics
E.A.H. Elamir & A.H. Seheult (2003) – Trimmed L-moments
J.R.M. Hosking (2007) – Some theory and practical uses of trimmed
L-moments
R. Serfling & P. Xiao (2007) – A contribution to multivariate
L-moments: L-comoment matrices
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
There are no reviews.
