LaBr

datasets

A lightweight, dependency-free toolbox for pre-processing XY
data from experimental methods (i.e. any signal that can be measured
along a continuous variable). This package provides methods for
baseline estimation and correction, smoothing, normalization,
integration and peaks detection. Baseline correction methods includes
polynomial fitting as described in Lieber and Mahadevan-Jansen (2003)
<doi:10.1366/000370203322554518>, Rolling Ball algorithm after Kneen
and Annegarn (1996) <doi:10.1016/0168-583X(95)00908-6>, SNIP algorithm
after Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, 4S Peak
Filling after Liland (2015) <doi:10.1016/j.mex.2015.02.009> and more.

Nicolas Frerebeau

alkahest

Pre-Processing XY Data from Experimental Methods

Brice Lebrun

Université Bordeaux Montaigne 

CNRS 

LaBr function

A <code><a href="/link/data.frame?package=alkahest&version=1.3.0" data-mini-rdoc="alkahest::data.frame">data.frame</a></code> with 1024 rows (channels) and 2 variables.<dl>
<dt>energy</dt>
<dd>(keV)</dd><dt>count</dt>
<dd></dd>
</dl>

Format

Gamma-Ray Spectrometry — LaBr

A <code><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> with 1024 rows (channels) and 2 variables.<dl>
<dt>energy</dt>
<dd>(keV)</dd>

<dt>count</dt>
<dd></dd>


</dl>

Gamma-Ray Spectrometry

LaBr: Gamma-Ray Spectrometry

Description

Usage

Arguments

Format

See Also

Examples