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A collection of functions for plotting spectra (NMR, IR etc) and carrying out various forms of top-down exploratory data analysis, such as HCA, PCA and model-based clustering. The design allows comparison of data from samples which fall into groups such as treatment vs. control. Robust methods appropriate for this type of high-dimensional data are available. ChemoSpec is designed to be very user friendly and suitable for people with limited background in R.


MALDIquant provides a complete analysis pipeline for MALDI-TOF and other mass spectrometry data. Distinctive features include baseline subtraction methods such as TopHat or SNIP, peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions.


The package provides data sets used in the book "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences" by Ron Wehrens, Springer (2011).


The package provides functions and scripts used in the book "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences" by Ron Wehrens, Springer (2011).


This package provides Partial least squares Regression for (weighted) generalized linear models and kfold crossvalidation of such models using various criteria. It allows for missing data in the eXplanatory variables. Bootstrap confidence intervals constructions are also available.


Organic/biological mass spectrometry data analysis.


Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.


This package is an interface to handle hyperspectral data sets in R. 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.


Likelihood inference based on higher order approximations for nonlinear models with possibly non constant variance


This package provides functions for fitting a Sparse Partial Least Squares Regression and Classification