Package: tea 1.1

Johannes Ossberger

tea: Threshold Estimation Approaches

Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.

Authors:Johannes Ossberger

tea_1.1.tar.gz
tea_1.1.tar.gz(r-4.5-noble)tea_1.1.tar.gz(r-4.4-noble)
tea_1.1.tgz(r-4.4-emscripten)tea_1.1.tgz(r-4.3-emscripten)
tea.pdf |tea.html
tea/json (API)

# Install 'tea' in R:
install.packages('tea', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • danish - Danish Fire Insurance Claims

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.89 score 77 scripts 197 downloads 24 exports 2 dependencies

Last updated 5 years agofrom:d0fb4bd084. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 16 2024
R-4.5-linuxOKDec 16 2024

Exports:althillavhilldAMSEdanielssondgpdDKeyeggplotGHgomesgpdFithallHimpHWmindistpgpdPSqgpdqqestplotqqgpdrgpdRTsumplotTH

Dependencies:latticeMatrix