**alkahest** is 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). It provides methods
for baseline estimation and correction, smoothing, normalization,
integration and peaks detection.

```
To cite alkahest in publications use:
Frerebeau N (2023). _alkahest: Pre-Processing XY Data from
Experimental Methods_. Université Bordeaux Montaigne, Pessac, France.
doi:10.5281/zenodo.7081524 <https://doi.org/10.5281/zenodo.7081524>,
R package version 1.1.1, <https://packages.tesselle.org/alkahest/>.
Une entrée BibTeX pour les utilisateurs LaTeX est
@Manual{,
author = {Nicolas Frerebeau},
title = {{alkahest: Pre-Processing XY Data from Experimental Methods}},
year = {2023},
organization = {Université Bordeaux Montaigne},
address = {Pessac, France},
note = {R package version 1.1.1},
url = {https://packages.tesselle.org/alkahest/},
doi = {10.5281/zenodo.7081524},
}
This package is a part of the tesselle project
<https://www.tesselle.org>.
```

You can install the released version of **alkahest**
from CRAN with:

`install.packages("alkahest")`

And the development version from GitHub with:

```
# install.packages("remotes")
::install_github("tesselle/alkahest") remotes
```

```
## Load the package
library(alkahest)
```

**alkahest** expects the input data to be in the
simplest form (a two-column matrix or data frame, a two-element list or
two numeric vectors).

```
## X-ray diffraction
data("XRD")
## 4S Peak Filling baseline
<- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)
baseline
plot(XRD, type = "l", xlab = expression(2*theta), ylab = "Count")
lines(baseline, type = "l", col = "red")
```

```
## Correct baseline
<- signal_drift(XRD, lag = baseline, subtract = TRUE)
XRD
## Find peaks
<- peaks_find(XRD, SNR = 3, m = 11)
peaks
plot(XRD, type = "l", xlab = expression(2*theta), ylab = "Count")
lines(peaks, type = "p", pch = 16, col = "red")
```

```
## Simulate data
set.seed(12345)
<- seq(-4, 4, length = 100)
x <- dnorm(x)
y <- y + rnorm(100, mean = 0, sd = 0.01) # Add some noise
z
## Plot raw data
plot(x, z, type = "l", xlab = "", ylab = "", main = "Raw data")
lines(x, y, type = "l", lty = 2, col = "red")
## Savitzky–Golay filter
<- smooth_savitzky(x, z, m = 21, p = 2)
smooth plot(smooth, type = "l", xlab = "", ylab = "", main = "Savitzky–Golay filter")
lines(x, y, type = "l", lty = 2, col = "red")
```

Please note that the **alkahest** project is released
with a Contributor Code
of Conduct. By contributing to this project, you agree to abide by
its terms.