In science, one man’s noise is another man’s signal – (Ng, 1990)
1 Preamble
prospectr provides a set of tools for signal processing and chemometrics, particularly for the pre-processing and sample selection of spectral data. It is increasingly used in spectroscopic applications, as reflected by the growing number of scientific publications citing the package.
Although similar functions are available in other packages, such as signal, many functions in prospectr are designed to work consistently with data.frame, matrix, and vector inputs. In addition, several functions are optimised for speed and rely on C++ code through the Rcpp and RcppArmadillo packages.
2 Introduction
Several spectroscopic techniques such as Near-Infrared (NIR) spectroscopy are high-throughput, non-destructive, and low-cost sensing methods with applications in agricultural, medical, food, and environmental science. A number of R packages relevant to spectroscopists are already available for processing and analysis of spectroscopic data. Since the publication of the special volume on Spectroscopy and Chemometrics in R (Mullen and Stokkum, 2007), many spectroscopy-related R packages have been released. Several are listed in relevant CRAN Task Views, including:
In addition, Bryan Hanson maintains a curated list of free and open-source software (FOSS) for spectroscopic applications; see https://bryanhanson.github.io/FOSS4Spectroscopy/.
3 Citing the package
If you use prospectr in your work, please cite it. The recommended citation can be obtained in R with:
citation(package = "prospectr")To cite package 'prospectr' in publications use:
Antoine Stevens and Leornardo Ramirez-Lopez (2026). An introduction
to the prospectr package. R package Vignette R package version 0.2.9.
A BibTeX entry for LaTeX users is
@Manual{,
title = {An introduction to the prospectr package},
author = {Antoine Stevens and Leornardo Ramirez-Lopez},
publication = {R package Vignette},
year = {2026},
note = {R package version 0.2.9},
}4 Further reading
The functionality of prospectr is documented in two additional vignettes:
- Signal processing: pre-processing methods including smoothing, derivatives, scatter corrections, baseline removal, and resampling.
- Calibration sampling: algorithms for selecting representative calibration and validation subsets from spectral data.