CRAN Task View: Chemometrics and Computational Physics
|Contact:||katharine.mullen at stat.ucla.edu|
|Contributions:||Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.|
|Citation:||Katharine Mullen (2022). CRAN Task View: Chemometrics and Computational Physics. Version 2022-09-29. URL https://CRAN.R-project.org/view=ChemPhys.|
|Installation:||The packages from this task view can be installed automatically using the ctv package. For example, |
ctv::install.views("ChemPhys", coreOnly = TRUE) installs all the core packages or
ctv::update.views("ChemPhys") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details.
Chemometrics and computational physics are concerned with the analysis of data arising in chemistry and physics experiments, as well as the simulation of physico-chemico systems. Many of the functions in base R are useful for these ends.
The second edition of Chemometrics with R: Multivariate Data Analysis in the Natural and Life Sciences by Ron Wehrens, ISBN 978-3-662-62027-4, Springer, 2020, provides an introduction to multivariate statistics in the life sciences, as well as coverage of several specific topics from the area of chemometrics. The associated package ChemometricsWithR facilitates reproduction of the examples in the book.
The book Modern Statistical Methods for Astronomy With R Applications by Eric D. Feigelson and G. Jogesh Babu, ISBN-13: 9780521767279, Cambridge, 2012, provides an introduction to statistics for astronomers and an overview of the foremost methods being used in astrostatistical analysis, illustrated by examples in R.
The book by Kurt Varmuza and Peter Filzmoser, Introduction to Multivariate Statistical Analysis in Chemometrics, ISBN 978-1-420-05947-2, CRC Press, 2009, is associated with the package chemometrics.
A special issue of R News with a focus on R in Chemistry was published in August 2006. A special volume of Journal of Statistical Software (JSS) dedicated to Spectroscopy and Chemometrics in R was published in January 2007.
Please e-mail the maintainer, submit an issue or pull request in the GitHub repository linked above, if we have omitted something of importance, or if a new package or function should be mentioned here.
Linear Regression Models
- Linear models can be fitted (via OLS) with
lm() (from stats). A least squares solution for
Ax = b can also be computed as
- The package nnls provides a means of constraining
x to non-negative or non-positive values; the package bvls allows other bounds on
x to be applied.
- Functions for isotonic regression are available in the package Iso, and are useful to determine the unimodal vector that is closest to a given vector
x under least squares criteria.
- Heteroskedastic linear models can be fit using the
gls() function of the nlme package.
Nonlinear Regression Models
nls() function (from stats) as well as the package minpack.lm allow the solution of nonlinear least squares problems.
- Correlated and/or unequal variances can be modeled using the
gnls() function of the nlme package and by nlreg.
- The PTAk package provides functions for Principal Tensor Analysis on k modes. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP.
- Multivariate curve resolution alternating least squares (MCR-ALS) is implemented in the package ALS.
- The alsace package provides MCR-ALS support for Liquid chromatography with PhotoDiode Array Detection (LC-DAD) data with many injections, with features for peak alignment and identification.
- The package drc provides functions for the analysis of one or multiple non-linear curves with focus on models for concentration-response, dose-response and time-response data.
- The package mdatools provides functions for MCR-ALS with constraints and a purity based methods similar to SIMPLISMA.
Partial Least Squares
- The package pls implements Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR).
- The package lspls implements the least squares-partial least squares (LS-PLS) method.
- Sparse PLS is implemented in the package spls package.
- The gpls package implements generalized partial least squares, based on the Iteratively ReWeighted Least Squares (IRWLS) method of Brian Marx.
- The package enpls implements ensemble partial least squares, a framework for measuring feature importance, outlier detection, and ensemble modeling based on (sparse) partial least squares regressions.
- The package mdatools provides functions for both PLS regression and discriminant analysis (PLSDA), including numerous plots, Jack-Knifing inference for regression coefficients and many other supplementary tools.
Principal Component Analysis
- Principal component analysis (PCA) is in the package stats as functions
princomp(). Some graphical PCA representations can be found in the psy package.
- The homals package provides nonlinear PCA and, by defining sets, nonlinear canonical correlation analysis (models of the Gifi-family).
- A desired number of robust principal components can be computed with the pcaPP package. The package elasticnet is applicable to sparse PCA.
- The subselect provides a collection of functions which assess the quality of variable subsets as surrogates for a full data set.
- The package mdatools provides functions for PCA analysis. The functions work both with conventional datasets as well as with images, including spectral images. A randomized version of PCA can be used to speed up calculations for large datasets. The package also implements PCA based classification method, Soft Independent Modelling of Class Analogy (SIMCA).
- Factor analysis (FA) is in the package stats as functions
factanal(); see Psychometrics task view for details on extensions.
Compositional Data Analysis
- The package compositions provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations). See also the book, Analyzing Compositional Data with R by K. Gerald von den Boogaart und Raimon Tolosana-Delgado, ISBN: 978-3-642-36808-0, Springer, 2013.
Independent Component Analysis
- Independent component analysis (ICA) can be computed using fastICA.
- The Cluster task view provides a list of packages that can be used for clustering problems.
- Stepwise variable selection for linear models, using AIC, is available in function
step(); package leaps implements leaps-and-bounds variable selection, by default using Mallow’s Cp. stepPlr provides stepwise variable selection for penalized logistic regression.
- Package varSelRF provides variable selection methods for random forests. Package clustvarsel implements variable selection for model-based clustering.
- The BioMark package implements two meta-methods for variable selection: stability selection (applying a primary selection method like a t-test, VIP value or PLSDA regression coefficient) to different subsets of the data, and higher criticism, which provides a data-driven choice of significance cutoffs in statistical testing.
- The package mdatools implements methods based on VIP scores, Selectivity Ratio (SR) as well as interval PLS.
- The kohonen package implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion. The som package provides functions for self-organizing maps.
- The units package attaches unit metadata to vectors, matrices and arrays, providing automatic propagation, conversion, derivation and simplification of units.
- The errors attaches uncertainty metadata to vectors, matrices and arrays, providing automatic propagation and reporting.
- The constants package provides values of the fundamental physical constants based on values reported by the Committee on Data for Science and Technology (CODATA), an interdisciplinary committee of the International Council for Science.
- NISTunits also provides values of the fundamental physical constants. The values it contains are based on the values reported by the National Institute of Standards and Technology, (NIST).
- The measurements contains tools to make working with physical measurements easier, such as functions to convert between metric and imperial units, or to calculate a dimension’s unknown value from other dimensions’ measurements.
- The metRology package provides support for metrology applications, including measurement uncertainty estimation and inter-laboratory metrology comparison studies.
- The ATmet (archived) package provides functions for smart sampling and sensitivity analysis for metrology applications, including computationally expensive problems.
- The investr package facilitates calibration/inverse estimation with linear and nonlinear regression models.
- The chemCal package provides functions for plotting linear calibration functions and estimating standard errors for measurements.
- The nlreg package is useful for nonlinear calibration models.
- The simecol package includes functions for cellular automata modeling.
- The CHNOSZ package provides functions for calculating the standard Gibbs energies and other thermodynamic properties, and chemical affinities of reactions between species contained in a thermodynamic database.
- The IAPWS95 package provides functions to calculate the thermodynamic properties of water according to the International Association for the Properties of Water and Steam (IAPWS) 1995 formulations.
Interfaces to External Libraries
- The package rcdk allows the user to access functionality in the Chemistry Development Kit (CDK), a Java framework for cheminformatics. This allows the user to load molecules, evaluate fingerprints (via the package fingerprint), calculate molecular descriptors and so on. In addition, the CDK API allows the user to view structures in 2D. The rcdklibs package provides the CDK libraries for use in R.
- ChemmineR is a cheminformatics toolkit for analyzing small molecules in R. Its add-on packages include fmcsR for mismatch tolerant maximum common substructure matching, eiR for accelerated structure similarity searching; bioassayR for analyzing bioactivity data, and ChemmineOB for accessing OpenBabel functionalities from R.
- The webchem package allows users to retrieve chemical information from various sources on the web and to interact with various APIs. Sources include: Chemical Identifier Resolver , ChemSpider , PubChem , Chemical Translation Service , PAN Pesticide Database , Alan Wood’s Compendium of Pesticide Common Names , PHYSPROP Database , ETOX , PPDB , and ChemIDplus .
- Bryan Hanson has compiled a broad range of Free and Open Source Software (FOSS) for Spectroscopy, much of which is in the form of R packages.
- The spectralAnalysis package allows users to pre-process, visualize and analyze spectroscopy data. Non-negative matrix factorization analysis is included.
- The ChemoSpec package collects user-friendly functions for plotting spectra (NMR, IR, etc) and carrying top-down exploratory data analysis, such as HCA, PCA and model-based clustering.
- The HyperChemoBridge interconverts ChemoSpec (and hyperSpec) objects
- The speaq package implements the hierarchical Cluster-based Peak Alignment (CluPA) and may be used for aligning NMR spectra.
- The package TIMP (archived) provides a problem solving environment for fitting separable nonlinear models in physics and chemistry applications, and has been extensively applied to time-resolved spectroscopy data.
- The package ChemoSpec2D allows exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra.
- The spectrino package provides tools for spectra viewing and organization.
- chromatographR (archived) provides an interface to load and analyze simple chromatography data such as HPLC-DAD/UV or GC-FID.
- chromConverter provides parsers to read chromatographic data into R. It currently supports Agilent Chemstation and Masshunter files as well as a growing list of text-based formats.
- The MSnbase defines infrastructure for mass spectrometry-based proteomics data handling, plotting, processing and quantification.
- The MALDIquant provides tools for quantitative analysis of MALDI-TOF mass spectrometry data, with support for baseline correction, peak detection and plotting of mass spectra.
- The OrgMassSpecR package is for organic/biological mass spectrometry, with a focus on graphical display, quantification using stable isotope dilution, and protein hydrogen/deuterium exchange experiments.
- The Bioconductor packages MassSpecWavelet, PROcess, and xcms are designed for the analysis of mass spectrometry data.
- The apLCMS package is designed for the processing of LC/MS based metabolomics data.
- The xMSanalyzer package allows merging apLCMS sample processing results from multiple sets of parameter settings, among other features.
- The MSPrep package is for post-processing of metabolomic data, including summarization of replicates, filtering, imputation, and normalization.
- The metaMS package is an MS-based metabolomics data processing and compound annotation pipeline.
Functional Magnetic Resonance Imaging
- The package fmri contains functions to analyze fMRI data using adaptive smoothing procedures.
Fluorescence Lifetime Imaging Microscopy
- Functions for visualization and analysis of Fluorescence Lifetime Imaging Microscopy (FLIM) datasets are available in the package TIMP (archived).
Fluorescence Excitation-Emission Matrix (EEM)
- The EEM reads raw EEM data and prepares it for further analysis.
- The package Bchron creates chronologies based on radiocarbon and non-radiocarbon dated depths.
Astronomy and astrophysics
- The astrodatR package collects 19 datasets from contemporary astronomy research, many of which are described in the aforementioned textbook ‘Modern Statistical Methods for Astronomy with R Applications’.
- The RobPer package calculates periodograms based on (robustly) fitting periodic functions to light curves.
- The package snapshot contains functions for reading and writing N-body snapshots from the GADGET code for cosmological N-body/SPH simulations.
- The package UPMASK performs unsupervised photometric membership assignment in stellar clusters using, e.g., photometry and spatial positions.
- The solaR package provides functions to determine the movement of the sun from the earth and to determine incident solar radiation.
- The FITSio package provides utilities to read and write files in the FITS (Flexible Image Transport System) format, a standard format in astronomy.
- The stellaR package manages and displays stellar tracks and isochrones from the Pisa low-mass database.
- The astroFns provides miscellaneous astronomy functions, utilities, and data.
- The cosmoFns contains standard expressions for distances, times, luminosities, and other quantities useful in observational cosmology, including molecular line observations.
- The celestial package includes a number of common astronomy conversion routines, particularly the HMS and degrees schemes.
- The SCEPtER package is used to estimate stellar mass and radius given observational data of effective temperature, [Fe/H], and astroseismic parameters.
- The lira package performs Bayesian linear regression and forecasting in Astronomy, accounting for all kinds of errors and correlations in the data.
- The SPADAR package provides functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems, including on-the-fly system conversion.
- The SCEPtERbinary allows for estimating the stellar age for double-lined detached binary systems, adopted from the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars.
- The Astrostatistics and Astroinformatics Portal is an R-centric collection of information regarding statistical analysis in astronomy.
- Hans Werner Borchers has a page on Astronomy modules and links for R, Python, and Julia.
- The solaR package provides functions to simulate and model systems involved in the capture and use of solar energy, including photovoltaics.
Water and Soil Chemistry
- The AquaEnv package is a toolbox for aquatic chemical modelling focused on (ocean) acidification and CO2 air-water exchange.
- See the Environmetrics task view for further related packages related to water and soil chemistry.
- The titrationCurves package provides functions to plot acid/base, complexation, redox, and precipitation titration curves.
- The eChem package provides functions to simulate voltammetry, chronoamperometry and chronocoulometry experiments, which may be useful in courses in analytical chemistry.
- The package radsafer provides functions for radiation safety; the package RadData provides nuclear decay data for dosimetric calculations from the International Commission on Radiological Protection.
|Core:||ALS, chemCal, Iso, kohonen, nnls, pls, PTAk.|
|Regular:||AquaEnv, astrodatR, astroFns, Bchron, BioMark, bvls, celestial, chemometrics, ChemoSpec, ChemoSpec2D, CHNOSZ, chromConverter, clustvarsel, compositions, constants, cosmoFns, drc, eChem, EEM, elasticnet, enpls, errors, fastICA, fingerprint, FITSio, fmri, homals, IAPWS95, investr, leaps, lira, lspls, MALDIquant, mdatools, measurements, metRology, minpack.lm, NISTunits, nlme, nlreg, OrgMassSpecR, pcaPP, psy, RadData, radsafer, rcdk, rcdklibs, RobPer, SCEPtER, SCEPtERbinary, simecol, snapshot, solaR, som, SPADAR, speaq, spectralAnalysis, spectrino, spls, stellaR, stepPlr, subselect, titrationCurves, units, UPMASK, varSelRF, webchem.|
|Archived:||ATmet, chromatographR, TIMP.|