Intensity Estimation on Geometric Networks with Penalized Splines
Overview
Fitting the intensity function of point processes on geometric networks. The method makes use of generalized additive models (GAM). The method is described in Marc Schneble, Göran Kauermann. "Intensity estimation on geometric networks with penalized splines." The Annals of Applied Statistics, 16(2) 843-865 June 2022. doi:10.1214/21-AOAS1522. A new class for representing linear networks as geometric networks is the core of the package.
- selective exporting of functions
- checking for inputs
From NEWS.md in the release
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- Help pages
- 31
- Examples that run
- 100%8 run in a plain check
- Exported functions with examples
- 78%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 10%
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- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2022-12-12requires archived package 'spatstat.core'
- 0.7.32022-07-11 · diff ↗ ·
removed 16 exports
bin_data, bspline_design, bspline_design_plot, confidence_band, fisher, fit_poisson_model, incidence, internal, network_bins, network_integral, network_intensity, network_knots, network_location, network_penalty, score, scoring - RR 4.2.0 released · 2022-04-22
- 0.6.02021-08-06 · diff ↗ ·
added 7 exports
delta_h_global, intensity_kernel, network_ISE, network_integral, network_intensity, network_location, rgnpp - 0.1.12021-06-07
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2021-06-07
- Total releases
- 3 / 5 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 471 KB / 2 files
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