L-moments (Python and Excel)

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These Python and Excel models offer high accuracy through precise L-Moments calculations and fitting algorithms, complemented by visualization tools for model validation and interpretation. Usage involves preparing your dataset, calculating L-Moments, fitting distributions, evaluating model fit with diagnostic plots, and generating visual comparisons to empirical data.

Key Features:

Comprehensive Distribution Fitting: Supports a variety of distributions including Generalized Extreme Value, Generalized Logistic, Generalized Pareto, Gumbel, and Pearson III.
Enhanced Robustness: L-Moments provide superior resistance to outliers, ensuring more reliable parameter estimation for skewed or heavy-tailed distributions.
Automated Workflow: Streamlined process for calculating L-Moments and fitting distributions, with minimal user intervention required.
High Accuracy: Implements precise algorithms to calculate L-Moments and fit distributions, ensuring high accuracy in modeling real-world data.
Visualization Tools: Integrated plotting functions to visualize the fitted distributions against empirical data, aiding in model validation and interpretation.

Usage:

Data Preparation: Input your dataset in a compatible format.
L-Moments Calculation: Utilize the provided functions to compute L-Moments from your data.
Distribution Fitting: Fit various distributions to the data using the computed L-Moments.
Model Evaluation: Assess the goodness-of-fit with diagnostic plots and statistical measures.
Visualization: Generate visual representations of the fitted distributions to compare against your empirical data.

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You'll get the Python and Excel models to fit extreme value distributions using L-moments, which is highly useful in hydrology and water management.