---
title: "Brief methodological background"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Brief methodological background}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

This vignette gives only the background needed to understand the software
interface. It is not a replacement for a methodological article.

## Histogram-based density estimation

Histogram-based estimators approximate a density by aggregating observations
into bins. Frequency polygon estimators smooth the histogram idea through linear
interpolation between neighboring bin heights. Averaged shifted histograms use
several shifted grids and average the resulting estimates.

These ideas are discussed in standard references on multivariate density
estimation, including Scott (1992), and in work on nonparametric density
estimation bounds such as Terrell and Scott (1985).

## Estimators implemented in the package

The package separates pointwise and grid-based evaluation.

| Estimator | Pointwise function | Grid function |
|---|---|---|
| Averaged Shifted Histogram | `ASH()` | `ASH_estimate()` |
| Linear Blend Frequency Polygon | `LBFP()` | `LBFP_estimate()` |
| General Linear Blend Frequency Polygon | `GLBFP()` | `GLBFP_estimate()` |

The pointwise functions evaluate the density at one supplied location. The
grid functions evaluate the same estimator on a regular or user-supplied grid.
They are intended for visualization and reproducible numerical summaries.

## Bandwidth and shift parameters

The bandwidth vector `b` controls the scale of the bins. Smaller values can
increase local variation, while larger values can smooth the estimate. The
helper `compute_bi_optim()` provides a plug-in starting value based on the
optimal cell-width calculation for multivariate frequency polygons in Carbon
and Duchesne (2024).

The shift vector `m` must contain positive integers. Larger values increase the
number of shifted components and therefore increase computational cost.

## Scope of this package

The package provides an R implementation, documentation, examples, tests,
pkgdown articles, and benchmark scaffolding for GLBFP workflows. This vignette
does not claim a new theoretical result.

## References

Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and
Visualization. Wiley. doi:10.1002/9780470316849.

Carbon, M., and Duchesne, T. (2024). Multivariate frequency polygon for
stationary random fields. Annals of the Institute of Statistical Mathematics,
76(2), 263-287. doi:10.1007/s10463-023-00883-5.

Terrell, G. R., and Scott, D. W. (1985). Oversmoothed Nonparametric Density
Estimates. Journal of the American Statistical Association, 80(389), 209-214.
doi:10.1080/01621459.1985.10477163.

The complete bibliographic record for the original GLBFP methodological article
has not yet been verified in this repository. It should be added before using
this vignette as source material for a journal article.
