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.

Contains a set of methods for fitting models and methods for
    validating the resulting models. The statistical methodologies comprise
    a comprehensive collection of approaches whose validity and utility have
    been accepted by experts in the Cheminformatics field. As promising new
    methodologies emerge from the statistical and data-mining communities, they
    will be incorporated into the laboratory. These methods are aimed at discovering
    quantitative structure-activity relationships (QSARs). However, the user can
    directly input their own choices of descriptors and responses, so the capability
    for comparing models is effectively unlimited.

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.