Package: AGCA4extremes 0.1.0

AGCA4extremes: Anchored Geodesic Component Analysis for Extremes

Implements anchored geodesic component analysis for multivariate extremes. The package provides Pareto and rank-Pareto marginal standardization, top-k angular extraction, anchored eigensolutions, reconstruction, diagnostics, nonparametric bootstrap tools, and plotting methods for benchmark-relative angular variation.

Authors:Alberto Quaini [aut, cre]

AGCA4extremes_0.1.0.tar.gz
AGCA4extremes_0.1.0.zip(r-4.7)AGCA4extremes_0.1.0.zip(r-4.6)AGCA4extremes_0.1.0.zip(r-4.5)
AGCA4extremes_0.1.0.tgz(r-4.6-x86_64)AGCA4extremes_0.1.0.tgz(r-4.6-arm64)AGCA4extremes_0.1.0.tgz(r-4.5-x86_64)AGCA4extremes_0.1.0.tgz(r-4.5-arm64)
AGCA4extremes_0.1.0.tar.gz(r-4.7-arm64)AGCA4extremes_0.1.0.tar.gz(r-4.7-x86_64)AGCA4extremes_0.1.0.tar.gz(r-4.6-arm64)AGCA4extremes_0.1.0.tar.gz(r-4.6-x86_64)
AGCA4extremes_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
AGCA4extremes/json (API)

# Install 'AGCA4extremes' in R:

install.packages('AGCA4extremes', repos = c('https://a91quaini.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/a91quaini/agca4extremes/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

agca_10d_simulation - Simulated 10-dimensional AGCA example

On CRAN:

openblas cpp

2.70 score 2 scripts 23 exports 2 dependencies

Last updated from:609238c11f. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	153
linux-devel-x86_64	OK	125
source / vignettes	OK	170
linux-release-arm64	OK	171
linux-release-x86_64	OK	124
macos-release-arm64	OK	163
macos-release-x86_64	OK	192
macos-oldrel-arm64	OK	223
macos-oldrel-x86_64	OK	230
windows-devel	OK	100
windows-release	OK	91
windows-oldrel	OK	98
wasm-release	OK	108

Exports:agca agca_fit_directions agca_rank_summary agca_reconstruct agca_residual_risk agca_standardize agca_variation_explained anchor_sensitivity anchored_departures angular_functional_error bootstrap_agca canonical_anchor decluster_runs frechet_anchor normalize_rows pareto_from_cdf principal_anchor rank_pareto row_norms simulate_agca_10d sphere_distance tail_directions threshold_stability

Dependencies:Rcpp RcppArmadillo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Fit anchored geodesic component analysis	agca
Simulated 10-dimensional AGCA example	agca_10d_simulation
Fit AGCA to angular directions	agca_fit_directions
AGCA rank summary	agca_rank_summary
Reconstruct angular directions from leading AGCA components	agca_reconstruct
Residual risk by AGCA rank	agca_residual_risk
Marginal standardization for AGCA	agca_standardize
Anchored variation explained	agca_variation_explained
Anchor sensitivity diagnostics	anchor_sensitivity
Anchored departures	anchored_departures
Functional approximation errors from angular reconstruction	angular_functional_error
Nonparametric AGCA bootstrap	bootstrap_agca
Canonical anchor	canonical_anchor
Runs declustering for radial extremes	decluster_runs
Spherical Frechet anchor	frechet_anchor
Normalize matrix rows	normalize_rows
Pareto marginal standardization from supplied CDFs	pareto_from_cdf
Plot AGCA bootstrap summaries	plot.agca_bootstrap
Plot AGCA output	plot.agca_fit
Principal angular anchor	principal_anchor
Rank-Pareto marginal standardization	rank_pareto
Row Euclidean norms	row_norms
Simulate the 10-dimensional AGCA example design	simulate_agca_10d
Spherical geodesic distance	sphere_distance
Summarize AGCA bootstrap output	summary.agca_bootstrap
Top-k angular directions	tail_directions
Threshold stability diagnostics	threshold_stability