Extreme Value Analyses with Missing Data
Methods for objects of class "confint_gev"
Methods for objects of class "confint_return_level"
Days in a year or in a month
Methods for objects of class "evmissing"
Block maxima
Internal evmissing functions
evmissing: Extreme Value Analyses with Missing Data
GEV Bayesian Inference with Adjustment for Missing Data
GEV influence curves for return levels
GEV influence curves
GEV ML Inference with Adjustment for Missing Data
Return Level Inferences
Weighted GEV ML Inference with Adjustment for Missing Data
Methods for objects of class "return_level"
Simulate raw data
Performs likelihood-based extreme value inferences with adjustment for the presence of missing values based on Simpson and Northrop (2026). A Generalised Extreme Value distribution is fitted to block maxima using maximum likelihood estimation, with the location and scale parameters reflecting the numbers of non-missing raw values in each block. A Bayesian version is also provided. For the purposes of comparison, there are options to make no adjustment for missing values or to discard any block maximum for which greater than a percentage of the underlying raw values are missing. Example datasets containing missing values are provided.
Useful links