{ "_id": "6ac57612f511ae58c09a3eb4", "Package": "specProc", "Type": "Package", "Title": "Preprocessing Tools for Laser-Induced Breakdown Spectroscopy", "Version": "0.8.3.9000", "Authors@R": "person(\"Christian L.\", \"Goueguel\", , \"christian.goueguel@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"https://orcid.org/0000-0003-0521-3446\"))", "Description": "Tools for exploring, preprocessing and modeling\nspectroscopic data, developed for laser-induced breakdown\nspectroscopy (LIBS) and also applicable to other techniques\nsuch as Raman, infrared, and inductively coupled plasma optical\nemission spectroscopy. Covers the workflow from raw spectra to\ncalibrated results: peak finding and fitting, wavelength\ncalibration, baseline correction, normalization and scaling,\northogonal signal correction including orthogonal partial least\nsquares (OPLS; Trygg and Wold (2002) ),\nwavelength selection (variable importance in projection,\nselectivity ratio, interval partial least squares), calibration\ntransfer, calibration curves and figures of merit, and plasma\ndiagnostics (Boltzmann and Saha-Boltzmann plots, Stark\nbroadening). Includes robust tools for outlier detection and\nexploration: robust principal component analysis (Hubert,\nRousseeuw and Vanden Branden (2005)\n), robust partial least squares\nregression (Hubert and Vanden Branden (2003)\n) and principal component regression\n(Hubert and Verboven (2003) ) with their\nrobust cross-validation (Engelen and Hubert (2005)\n), robust classification by\nSIMCA (Vanden Branden and Hubert (2005)\n) and discriminant analysis\n(Hubert and Van Driessen (2004)\n), detection of deviating\ncells (Rousseeuw and Van den Bossche (2018)\n), PCA with cellwise\noutliers and missing values (MacroPCA; Hubert, Rousseeuw and\nVan den Bossche (2019) ),\nrobust transformations to central normality (Raymaekers and\nRousseeuw (2021) ), and\nself-organizing maps (Kohonen (1982) ).\nPreprocessing steps are also available as 'recipes' steps, and\nthe robust models as 'parsnip' models and engines, for\n'tidymodels' workflows. Computationally intensive steps are\nimplemented in 'C++' via 'Rcpp' and 'RcppEigen'.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "VignetteBuilder": "knitr", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "URL": "https://github.com/ChristianGoueguel/specProc,\nhttps://christiangoueguel.com/specProc/", "BugReports": "https://github.com/ChristianGoueguel/specProc/issues", "Language": "en-US", "Config/roxygen2/version": "8.1.0", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://christiangoueguel.r-universe.dev", "Date/Publication": "2026-10-05 21:55:40 UTC", "RemoteUrl": "https://github.com/ChristianGoueguel/specProc", "RemoteRef": "HEAD", "RemoteSha": "03cdf40dcb19f69c587da34925f27c869aa3beaa", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-06 22:13:49 UTC", "User": "root" }, "Author": "Christian L. Goueguel [aut, cre] (ORCID:\n)", "Maintainer": "Christian L. Goueguel ", "_user": "christiangoueguel", "_type": "src", "_file": "specProc_0.8.3.9000.tar.gz", "_fileid": "https://r2.ropensci.org/e2c8e24872add6c0bd0470487deece9def36e0dabdc3c5c73a08e2b7fd6e9a61", "_filesize": 6667181, "_sha256": "e2c8e24872add6c0bd0470487deece9def36e0dabdc3c5c73a08e2b7fd6e9a61", "_expires": "2027-01-14T22:28:31.000Z", "_created": "2026-10-06T22:13:49.000Z", "_published": "2026-10-06T22:28:34.669Z", "_buildurl": "https://github.com/r-universe/christiangoueguel/actions/runs/37538522791", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 112528532700, "time": 406, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "11447703920" }, { "job": 112528532621, "time": 498, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11447134448" }, { "job": 112528532704, "time": 410, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11448252632" }, { "job": 112528532716, "time": 488, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11447569292" }, { "job": 112528532748, "time": 329, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11448082892" }, { "job": 112528532803, "time": 673, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11447059990" }, { "job": 112528532633, "time": 474, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11447309151" }, { "job": 112528532686, "time": 753, "config": "macos-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11447304642" }, { "job": 112525647915, "time": 481, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11447597719" }, { "job": 112528532598, "time": 209, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11448460934" }, { "job": 112528532743, "time": 736, "config": "windows-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "11447504583" }, { "job": 112528532671, "time": 562, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11447998818" }, { "job": 112528532691, "time": 363, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11447803445" }, { "job": 112528532734, "time": 650, "config": "windows-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11447379377" }, { "job": 112528532810, "time": 296, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11448446186" } ], "_upstream": "https://github.com/ChristianGoueguel/specProc", "_commit": { "id": "03cdf40dcb19f69c587da34925f27c869aa3beaa", "author": "Christian L. Goueguel ", "committer": "Christian L. Goueguel ", "message": "Gap-segment derivative and binning of adjacent channels\n\ngap_derivative() computes the first or second gap-segment (Norris-Williams)\nderivative from the means of segments of `segment` channels separated by\n`gap` channels; step_gap_derivative() is its recipe step. bin_spectra()\nreplaces groups of `width` adjacent channels by their mean, within each\ndetector; step_bin_spectra() is its recipe step. Together with\nstep_spectral_norm(method = \"area\"), they reproduce the preprocessing of the\nwinning team of the EMSLIBS 2019 classification contest.\n\nThe detector segments of savitzky_golay() now come from a shared helper,\nsegment_columns(), also used by the new filters.\n", "time": 1791237340 }, "_maintainer": { "name": "Christian L. 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"title": "Apply a Wavelength Calibration", "topics": [ "apply_calibration" ] }, { "page": "average", "title": "Fast Average for Large Spectral Dataset", "topics": [ "average" ] }, { "page": "baseline_als", "title": "Asymmetric Least Squares", "topics": [ "baseline_als" ] }, { "page": "baseline_arpls", "title": "Asymmetrically Reweighted Penalized Least Squares", "topics": [ "baseline_arpls" ] }, { "page": "baseline_lambda", "title": "Baseline Smoothness Parameter", "topics": [ "baseline_lambda" ] }, { "page": "baseline_lsp", "title": "Least-Squares Polynomial", "topics": [ "baseline_lsp" ] }, { "page": "bin_spectra", "title": "Binning of Adjacent Channels", "topics": [ "bin_spectra" ] }, { "page": "biweight_location", "title": "Biweight Location", "topics": [ "biweight_location" ] }, { "page": "biweight_midcorrelation", "title": "Biweight Midcorrelation", "topics": [ "biweight_midcorrelation" ] }, { "page": "biweight_midcovariance", "title": "Biweight Midcovariance", "topics": [ 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}, { "page": "minmax", "title": "Min-Max Normalization", "topics": [ "minmax" ] }, { "page": "msc", "title": "Multiplicative Signal Correction", "topics": [ "msc" ] }, { "page": "multipeak_fit", "title": "Multiple Peaks Fitting", "topics": [ "multipeak_fit" ] }, { "page": "nas", "title": "Net Analyte Signal and Figures of Merit", "topics": [ "nas" ] }, { "page": "nist_ionization_energy", "title": "Ionization Energies from the NIST Atomic Spectra Database", "topics": [ "nist_ionization_energy" ] }, { "page": "nist_levels", "title": "Energy Levels and Partition Functions from the NIST Atomic Spectra Database", "topics": [ "nist_levels", "partition_function" ] }, { "page": "nist_lines", "title": "Atomic Line Data from the NIST Atomic Spectra Database", "topics": [ "nist_lines" ] }, { "page": "normalize", "title": "Spectra Normalization", "topics": [ "normalize" ] }, { "page": "num_intervals", "title": "Number of Intervals", "topics": [ "num_intervals" ] }, { "page": "o2pls", "title": "Modified Orthogonal Projections to Latent Structures", "topics": [ "o2pls" ] }, { "page": "opls", "title": "Orthogonal Projections to Latent Structures", "topics": [ "opls" ] }, { "page": "osc", "title": "Orthogonal Signal Correction", "topics": [ "osc" ] }, { "page": "pareto_scale", "title": "Pareto Scaling", "topics": [ "pareto_scale" ] }, { "page": "pds", "title": "Piecewise Direct Standardization", "topics": [ "pds" ] }, { "page": "peak_fit", "title": "Peak Fitting", "topics": [ "peak_fit" ] }, { "page": "plot_boltzmann", "title": "Draw a Boltzmann or Saha-Boltzmann Plot", "topics": [ "plot_boltzmann" ] }, { "page": "plot_calibration", "title": "Plot a Calibration Curve", "topics": [ "plot_calibration" ] }, { "page": "plot_cell_map", "title": "Cell Map of a MacroPCA or cellPCA Fit", "topics": [ "plot_cell_map" ] }, { "page": "plot_contributions", "title": "Plot Q and T-squared Contributions", "topics": [ "plot_contributions" ] }, { "page": "plot_coomans", "title": "Coomans Plot of 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