Package: GLBFP 0.5.2.9000
GLBFP: General Linear Blend Frequency Polygon Density Estimation
Implements nonparametric density estimation with Averaged Shifted Histogram (ASH), Linear Blend Frequency Polygon (LBFP), and General Linear Blend Frequency Polygon (GLBFP) estimators. The package provides pointwise and grid-based estimation workflows, sparse-prefix grid-count computation, fixed-grid leave-one-out self-support scores, plotting helpers, and plug-in bandwidth selection.
Authors:
GLBFP_0.5.2.9000.tar.gz
GLBFP_0.5.2.9000.zip(r-4.7-any)GLBFP_0.5.2.9000.zip(r-4.6-any)GLBFP_0.5.2.9000.zip(r-4.5-any)
GLBFP_0.5.2.9000.tgz(r-4.6-any)GLBFP_0.5.2.9000.tgz(r-4.5-any)
GLBFP_0.5.2.9000.tar.gz(r-4.7-any)GLBFP_0.5.2.9000.tar.gz(r-4.6-any)
GLBFP_0.5.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
GLBFP/json (API)
| # Install 'GLBFP' in R: |
| install.packages('GLBFP', repos = c('https://aureliennicosiaulaval.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/aureliennicosiaulaval/glbfp/issues
Pkgdown/docs site:https://aureliennicosiaulaval.github.io
Datasets:
- ashua - River Ashuapmushuan daily flow and level data
computational-statisticsdensity-estimationleave-one-outnonparametric-statisticsoutlier-detectionreproducible-research
Last updated from:cf9e1f5414. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 148 | ||
| source / vignettes | OK | 200 | ||
| linux-release | OK | 146 | ||
| macos-release | OK | 93 | ||
| macos-oldrel | OK | 77 | ||
| windows-devel | OK | 100 | ||
| windows-release | OK | 69 | ||
| windows-oldrel | OK | 78 | ||
| wasm-release | OK | 151 |
Exports:ashASHash_estimateASH_estimatecompute_bi_optimcompute_Dicompute_G_starG_iglbfpGLBFPglbfp_estimateGLBFP_estimateK_milbfpLBFPlbfp_estimateLBFP_estimate
Dependencies:askpassbase64encbslibcachemclicpp11crosstalkcurldata.tabledigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlazyevallifecyclemagrittrmemoisemimeopensslotelpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml
Leave-one-out D_i diagnostics
Last update: 2026-08-21
Started: 2026-06-07
Brief methodological background
Histogram-based density estimation | Estimators implemented in the package | Bandwidth and shift parameters | Scope of this package | References
Last update: 2026-06-08
Started: 2026-05-03
Choosing between ASH, LBFP and GLBFP
Practical starting rules
Last update: 2026-06-08
Started: 2026-06-07
Getting started with GLBFP
Installation | Load the package | Estimate density at one point | Estimate density on a grid | Using the included data | Leave-one-out scores | Input expectations | Where to go next
Last update: 2026-06-08
Started: 2026-05-03
Legacy estimation example
Last update: 2026-06-08
Started: 2025-02-20
Objects, summaries and plotting
Last update: 2026-06-08
Started: 2026-06-07
Package overview and workflow map
Estimator families | Core workflow | Article guide | Input expectations
Last update: 2026-06-08
Started: 2026-05-03
Sparse-prefix computation
Last update: 2026-06-08
Started: 2026-06-07
Two-dimensional density estimation
Simulated example | Pointwise estimation | Grid estimation | Visualization | Computational note
Last update: 2026-06-08
Started: 2026-05-03
Validation and comparison
Run benchmark suite | MISE ranking by scenario | Sensitivity analysis (b, m) | Notes
Last update: 2026-06-08
Started: 2026-05-03
