Package overview and workflow map

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This vignette gives a compact map of the package. For a first executable example, start with the “Getting started with GLBFP” vignette.

Estimator families

GLBFP implements three related histogram-based density estimator families.

Family	Pointwise function	Grid function	Main tuning inputs
Averaged Shifted Histogram	`ASH()`	`ASH_estimate()`	`b`, `m`
Linear Blend Frequency Polygon	`LBFP()`	`LBFP_estimate()`	`b`
General Linear Blend Frequency Polygon	`GLBFP()`	`GLBFP_estimate()`	`b`, `m`

Lowercase aliases are also available:

library(GLBFP)

c(
  ash = exists("ash"),
  lbfp = exists("lbfp"),
  glbfp = exists("glbfp")
)
#>   ash  lbfp glbfp 
#>  TRUE  TRUE  TRUE

The uppercase names are kept for compatibility with the original package API.

Core workflow

Most analyses follow the same sequence:

prepare a finite numeric matrix or data frame;
choose a bandwidth vector b;
choose m when using ASH or GLBFP;
evaluate a pointwise estimator or a grid estimator;
inspect the S3 summary, predictions, plots, and grid diagnostics.

x <- matrix(rnorm(300), ncol = 1)
b <- compute_bi_optim(x, m = 1)

point_fit <- glbfp(x = 0, data = x, b = b, m = 1)
grid_fit <- glbfp_estimate(data = x, b = b, m = 1, grid_size = 80)

summary(point_fit)
#> Method: GLBFP 
#> Dimension: 1 
#> Point: 0 
#> Estimation: 0.3867351 
#> Standard error: 0.0838592 
#> 95% CI: 0.362643259199835, 0.410826868862035 
#> Bandwidths (b): 0.155144970240404 
#> Shifts (m): 1 
#> Relative grid coordinate (u): 0.91613931912711 
#> Visited cells: 2 
#> Prefix nodes: 2
summary(grid_fit)
#> Method: GLBFP 
#> Dimension: 1 
#> Grid points: 80 
#> Grid type: rectangular 
#> Grid dimensions: 80 
#> Bandwidths (b): 0.155144970240404 
#> Shifts (m): 1 
#> Density range: 0.000828108031887938 to 0.514909698997431 
#> Density quartiles: 0.0553978740851781, 0.168558436679355, 0.338815771448144 
#> Density median: 0.1685584 
#> Density mean: 0.1908764 
#> Zero densities: 0 
#> Standard error median: 0.04918585 
#> Median visited cells: 2 
#> Median prefix nodes: 2

Article guide

Use the vignettes in this order when learning the package:

Article	Purpose
Getting started with GLBFP	First runnable examples and basic object usage
Package overview and workflow map	Orientation across estimators and workflows
Brief methodological background	Minimal statistical context and references
Choosing between ASH, LBFP and GLBFP	Practical estimator comparison
Two-dimensional density estimation	2D estimation and visualization
Sparse-prefix computation	Internal sparse grid-count diagnostics
Objects, summaries and plotting	S3 classes and helper methods
Validation and comparison	Lightweight benchmark scaffolding

Input expectations

The package expects finite numeric observations with one observation per row. Missing values should be removed or handled before estimation. For nearly constant data, provide explicit non-degenerate min_vals and max_vals.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.