Simple functions for plotting linear
	calibration functions and estimating standard errors for measurements
	according to the Handbook of Chemometrics and Qualimetrics: Part A
	by Massart et al. (1997) There are also functions estimating the limit
	of detection (LOD) and limit of quantification (LOQ).
	The functions work on model objects from - optionally weighted - linear
	regression (lm) or robust linear regression ('rlm' from the 'MASS' package).

A collection of functions that have been developed to assist
    experimenter in modeling chemical degradation kinetic data. The
    selection of the appropriate degradation model and parameter
    estimation is carried out automatically as far as possible and is
    driven by a rigorous statistical interpretation of the results.  The
    package integrates already available goodness-of-fit statistics for
    nonlinear models. In addition it allows data fitting with the
    nonlinear first-order multi-target (FOMT) model.

We aim to deal with the average treatment effect (ATE), where the data are
    subject to high-dimensionality and measurement error. This package primarily contains two 
    functions, which are used to generate artificial data and estimate ATE with high-dimensional 
    and error-prone data accommodated.

Quantify and visualise various measures of chemical diversity and
    dissimilarity, for phytochemical compounds and other sets of chemical
    composition data. Importantly, these measures can incorporate biosynthetic
    and/or structural properties of the chemical compounds, resulting in a more
    comprehensive quantification of diversity and dissimilarity. For details,
    see Petrén, Köllner and Junker (2023) <doi:10.1111/nph.18685>.

R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).

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.