Parametrically Guided Kernel Density Estimator for Spherical Data
Overview
Nonparametric density estimation for (hyper)spherical data by means of a parametrically guided kernel estimator (Alonso-Pena et al. (2024) doi:10.1111/sjos.12737. The package also allows the data-driven selection of the smoothing parameter and the representation of the estimated density for circular and spherical data. Estimators of the density without guide can also be obtained.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 4 (1 internal)
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 75%
Downloads
Dependencies
Code & Tests
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.0.2Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.0.12024-02-07
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2024-02-07
- Total releases
- 2 / 2 yrs
- License
- GPL (>= 2) OSI
- Download size
- 7.7 KB
Cite
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citation("pgKDEsphere")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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