Misc. Functions for Processing and Sample Selection of Spectroscopic Data
Antoine Stevens & Leo Ramirez-Lopez
Last update: 2024-02-16
Version: 0.2.7 – cakes
prospectr
is becoming more and more used in spectroscopic
applications, which is evidenced by the number of scientific
publications citing the package. This package is very useful for signal
processing and chemometrics in general as it provides various utilities
for pre–processing and sample selection of spectral data. While similar
functions are available in other packages, like
signal
, the functions in
this package works indifferently for data.frame
, matrix
and vector
inputs. Besides, several functions are optimized for speed and use C++
code through the Rcpp
and
RcppArmadillo
packages.
Install this package from github by:
remotes::install_github("l-ramirez-lopez/prospectr")
NOTE: in some MAC Os it is still recommended to install gfortran
and
clang
from here. Even
for R >= 4.0. For more info, check this
issue.
Check the NEWS document for new functionality and general changes in the package.
A vignette for prospectr
explaining its core functionality is
available at
https://CRAN.R-project.org/package=prospectr/vignettes/prospectr.html.
A vignette gives an overview of the main functions of the package. Just
type vignette("prospectr-intro")
in the console to access it.
Currently, the following preprocessing functions are available:
resample()
: resample a signal to new coordinates by linear or
spline interpolation
resample2()
: resample a signal to new coordinates using FWHM
values
movav()
: moving average
standardNormalVariate()
: standard normal variate
msc()
: multiplicative scatter correction
detrend()
: detrend normalization
baseline()
: baseline removal/correction
blockScale()
: block scaling
blockNorm()
: sum of squares block weighting
binning()
: average in column–wise subsets
savitzkyGolay()
: Savitzky-Golay filter (smoothing and
derivatives)
gapDer()
: gap-segment derivative
continuumRemoval()
: continuum-removed absorbance or reflectance
values
The selection of representative samples/observations for calibration of spectral models can be achieved with one of the following functions:
naes()
: k-means sampling
kenStone()
: CADEX (Kennard–Stone) algorithm
duplex()
: DUPLEX algorithm
shenkWest()
: SELECT algorithm
puchwein()
: Puchwein sampling
honigs()
: Unique-sample selection by spectral subtraction
Other useful functions are also available:
read_nircal()
: read binary files exported from BUCHI NIRCal
software
readASD()
: read binary or text files from an ASD instrument
(Indico Pro format)
spliceCorrection()
: correct spectra for steps at the splice of
detectors in an ASD FieldSpec Pro
cochranTest()
: detects replicate outliers with the Cochran C
test
Antoine Stevens and Leornardo Ramirez-Lopez (2022). An introduction to the prospectr package. R package Vignette R package version 0.2.4. A BibTeX entry for LaTeX users is:
@Manual{stevens2022prospectr,
title = {An introduction to the prospectr package},
author = {Antoine Stevens and Leornardo Ramirez-Lopez},
publication = {R package Vignette},
year = {2024},
note = {R package version 0.2.7},
}
You can send an email to the package maintainer
(ramirez.lopez.leo@gmail.com) or create an
issue on github.
To install the development version of prospectr
, simply install
devtools
from CRAN then
run install_github("l-ramirez-lopez/prospectr")
.
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