streamflow

Annual maximum streamflow measurements for extreme value analysis.

datasets

Provides generalized L-moments estimation methods for the
generalized extreme value ('GEV') distribution. Implements both stationary
'GEV' and non-stationary 'GEV11' models where location and scale parameters
vary with time. Includes various penalty functions ('Martins'-'Stedinger',
Park, Cannon, 'Coles'-Dixon) for shape parameter regularization.
Also provides model averaging estimation ('ma.gev') that combines MLE and
L-moment methods with multiple weighting schemes for robust high quantile
estimation. The 'GLME' methodology is described in Shin et al. (2025a)
<doi:10.48550/arXiv.2512.20385>. The non-stationary L-moment method
is based on Shin et al. (2025b) <doi:10.1007/s42952-025-00325-3>.
The model averaging method is described in Shin et al. (2026)
<doi:10.1007/s00477-025-03167-x>.
See also 'Hosking' (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x> for
L-moments theory and 'Martins' and 'Stedinger' (2000)
<doi:10.1029/1999WR900330> for penalized likelihood methods.

Yonggwan Shin

GLmom

Generalized L-Moments Estimation for Extreme Value Distributions

Seokkap Ko

Jihong Park

Yire Shin

Jeong-Soo Park

streamflow function

A data frame with 50 rows and 2 columns:<dl>
 <dt>Year</dt>
<dd>Year of observation (character)</dd> <dt>r1</dt>
<dd>Annual maximum streamflow value (numeric)</dd>
</dl>

Format

Streamflow Data — streamflow

A data frame with 50 rows and 2 columns:<dl>
 <dt>Year</dt>
<dd>Year of observation (character)</dd>

 <dt>r1</dt>
<dd>Annual maximum streamflow value (numeric)</dd>


</dl>

streamflow: Streamflow Data

Description

Usage

Arguments

Format

References

Examples