Showing 14 of total 14 results (show query)
l-ramirez-lopez
prospectr:Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data
Functions to preprocess spectroscopic data and conduct (representative) sample selection/calibration sampling.
Maintained by Leonardo Ramirez-Lopez. Last updated 12 days ago.
chemometricsderivativesinfrarednear-infrarednirpedometricspreprocessingresamplesamplingsignalsoil-spectroscopyspectroscopyopenblascppopenmp
42 stars 10.22 score 326 scripts 4 dependents
martin3141
spant:MR Spectroscopy Analysis Tools
Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.
Maintained by Martin Wilson. Last updated 1 days ago.
brainmrimrsmrshubspectroscopyfortran
25 stars 8.52 score 81 scripts
r-hyperspec
hyperSpec:Work with Hyperspectral Data, i.e. Spectra + Meta Information (Spatial, Time, Concentration, ...)
Comfortable ways to work with hyperspectral data sets, i.e. spatially or time-resolved spectra, or spectra with any other kind of information associated with each of the spectra. The spectra can be data as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS, etc. More generally, any data that is recorded over a discretized variable, e.g. absorbance = f(wavelength), stored as a vector of absorbance values for discrete wavelengths is suitable.
Maintained by Claudia Beleites. Last updated 10 months ago.
data-wranglinghyperspectralimaginginfrarednmrramanspectroscopyuv-visxrf
16 stars 8.10 score 233 scripts 2 dependents
bryanhanson
ChemoSpec:Exploratory Chemometrics for Spectroscopy
A collection of functions for top-down exploratory data analysis of spectral data including nuclear magnetic resonance (NMR), infrared (IR), Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy. Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate for this type of high-dimensional data are available. ChemoSpec is designed for structured experiments, such as metabolomics investigations, where the samples fall into treatment and control groups. Graphical output is formatted consistently for publication quality plots. ChemoSpec is intended to be very user friendly and to help you get usable results quickly. A vignette covering typical operations is available.
Maintained by Bryan A. Hanson. Last updated 1 years ago.
infrarednmrramanspectroscopyultravioletvisiblexrf
59 stars 7.60 score 45 scripts 1 dependents
ropensci
lightr:Read Spectrometric Data and Metadata
Parse various reflectance/transmittance/absorbance spectra file formats to extract spectral data and metadata, as described in Gruson, White & Maia (2019) <doi:10.21105/joss.01857>. Among other formats, it can import files from 'Avantes' <https://www.avantes.com/>, 'CRAIC' <https://www.microspectra.com/>, and 'OceanOptics'/'OceanInsight' <https://www.oceanoptics.com/> brands.
Maintained by Hugo Gruson. Last updated 2 months ago.
file-importreproducibilityreproducible-researchreproducible-sciencespectral-dataspectroscopy
13 stars 7.11 score 11 scripts 2 dependentsbryanhanson
readJDX:Import Data in the JCAMP-DX Format
Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats. The official JCAMP-DX site is <http://www.jcamp-dx.org/>.
Maintained by Bryan A. Hanson. Last updated 1 years ago.
8 stars 6.48 score 7 scripts 5 dependentsl-ramirez-lopez
resemble:Memory-Based Learning in Spectral Chemometrics
Functions for dissimilarity analysis and memory-based learning (MBL, a.k.a local modeling) in complex spectral data sets. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.
Maintained by Leonardo Ramirez-Lopez. Last updated 2 years ago.
chemoinformaticschemometricsinfrared-spectroscopylazy-learninglocal-regressionmachine-learningmemory-based-learningnirpedometricssoil-spectroscopyspectral-dataspectral-libraryspectroscopyopenblascppopenmp
20 stars 5.91 score 27 scripts
mooresm
serrsBayes:Bayesian Modelling of Raman Spectroscopy
Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.
Maintained by Matt Moores. Last updated 4 years ago.
bayesianchemometricsramansequential-monte-carlospectroscopycpp
8 stars 5.46 score 36 scripts
henningte
ir:Functions to Handle and Preprocess Infrared Spectra
Functions to import and handle infrared spectra (import from '.csv' and Thermo Galactic's '.spc', baseline correction, binning, clipping, interpolating, smoothing, averaging, adding, subtracting, dividing, multiplying, plotting).
Maintained by Henning Teickner. Last updated 3 years ago.
chemometricsinfraredinfrared-spectrair-packagemid-infrared-spectraspectroscopy
6 stars 5.32 score 35 scripts
jatanrt
eprscope:Processing and Analysis of Electron Paramagnetic Resonance Data and Spectra in Chemistry
Processing, analysis and plottting of Electron Paramagnetic Resonance (EPR) spectra in chemistry. Even though the package is mainly focused on continuous wave (CW) EPR/ENDOR, many functions may be also used for the integrated forms of 1D PULSED EPR spectra. It is able to find the most important spectral characteristics like g-factor, linewidth, maximum of derivative or integral intensities and single/double integrals. This is especially important in spectral (time) series consisting of many EPR spectra like during variable temperature experiments, electrochemical or photochemical radical generation and/or decay. Package also enables processing of data/spectra for the analytical (quantitative) purposes. Namely, how many radicals or paramagnetic centers can be found in the analyte/sample. The goal is to evaluate rate constants, considering different kinetic models, to describe the radical reactions. The key feature of the package resides in processing of the universal ASCII text formats (such as '.txt', '.csv' or '.asc') from scratch. No proprietary formats are used (except the MATLAB EasySpin outputs) and in such respect the package is in accordance with the FAIR data principles. Upon 'reading' (also providing automatic procedures for the most common EPR spectrometers) the spectral data are transformed into the universal R 'data frame' format. Subsequently, the EPR spectra can be visualized and are fully consistent either with the 'ggplot2' package or with the interactive formats based on 'plotly'. Additionally, simulations and fitting of the isotropic EPR spectra are also included in the package. Advanced simulation parameters provided by the MATLAB-EasySpin toolbox and results from the quantum chemical calculations like g-factor and hyperfine splitting/coupling constants (a/A) can be compared and summarized in table-format in order to analyze the EPR spectra by the most effective way.
Maintained by Ján Tarábek. Last updated 2 days ago.
chemistrydata-analysisdata-visualizationepresrfittingoptimizationprogramming-languagereproducible-researchscientific-plottingspectroscopyopenjdk
4.76 score 7 scripts
nanxstats
OHPL:Ordered Homogeneity Pursuit Lasso for Group Variable Selection
Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.
Maintained by Nan Xiao. Last updated 8 months ago.
chemometricshigh-dimensional-datahomogeneity-pursuitlassopartial-least-squares-regressionspectroscopyvariable-selection
7 stars 3.85 score 9 scripts
noemiallefs
andurinha:Make Spectroscopic Data Processing Easier
The goal of 'andurinha' is provide a fast and friendly way to process spectroscopic data. It is intended for processing several spectra of samples with similar composition (tens to hundreds of spectra). It compiles spectroscopy data files, produces standardized and second derivative spectra, finds peaks and allows to select the most significant ones based on the second derivative/absorbance sum spectrum. It also provides functions for graphic evaluation of the outputs.
Maintained by Noemi Alvarez Fernandez. Last updated 2 years ago.
peakspeaks-selectionspectroscopy
1 stars 3.70 score 6 scripts
aphalo
rOmniDriver:Omni Driver R wrapper
This package is a wrapper of the OmniDriver java driver for Ocean Optics spectrometers.
Maintained by Pedro J. Aphalo. Last updated 7 months ago.
data-acquisitionspectroscopyopenjdk
1 stars 3.00 score 6 scripts