{ "_id": "6ac8223dde46216d5edd4145", "Package": "geosmooth", "Title": "Geometric Methods for Nonparametric Regression and Density\nEstimation", "Version": "0.2.0", "Authors@R": "c(\nperson(given = \"Pawel\",\nfamily = \"Gajer\",\nemail = \"pgajer@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(given = \"Gael\",\nfamily = \"Guennebaud\",\nrole = \"ctb\",\ncomment = \"Eigen\"),\nperson(given = \"Benoit\",\nfamily = \"Jacob\",\nrole = \"ctb\",\ncomment = \"Eigen\"),\nperson(given = \"Authors of\",\nfamily = \"Eigen\",\nrole = \"cph\",\ncomment = \"Authorship and copyright in the included Eigen library\"),\nperson(given = \"Yixuan\",\nfamily = \"Qiu\",\nrole = c(\"ctb\", \"cph\"),\ncomment = \"Spectra\"),\nperson(given = \"Contributors to\",\nfamily = \"Spectra\",\nrole = \"cph\",\ncomment = \"Copyright in the included Spectra library\"),\nperson(given = \"Sunil\",\nfamily = \"Arya\",\nrole = c(\"ctb\", \"cph\"),\ncomment = \"ANN\"),\nperson(given = \"David M.\",\nfamily = \"Mount\",\nrole = c(\"ctb\", \"cph\"),\ncomment = \"ANN\"),\nperson(given = \"University of\",\nfamily = \"Maryland\",\nrole = \"cph\",\ncomment = \"ANN\")\n)", "Description": "Provides geometric methods for nonparametric regression\nand density estimation on data represented as coordinate\nmatrices or weighted graphs. Methods include local polynomial\nsmoothing, model-averaged local polynomial smoothing, local\npolynomial lifting trend filtering, synchronized local\npolynomial lifting trend filtering, graph low-pass filtering,\nand Hessian-energy regression. Methodological references\ninclude Gajer and Ravel (2025) \"Adaptive Geometric Regression\nfor High-Dimensional Structured Data\"\n, Fan and Gijbels (1996,\nISBN:9780412983214), Wang et al. (2016) \"Trend Filtering on\nGraphs\" , and Kim\net al. (2009) \"Semi-Supervised Regression Using Hessian Energy\"\n.", "Copyright": "file inst/COPYRIGHTS", "License": "GPL (>= 3)", "URL": "https://pgajer.github.io/geosmooth/,\nhttps://github.com/pgajer/geosmooth", "BugReports": "https://github.com/pgajer/geosmooth/issues", "Encoding": "UTF-8", "Language": "en-US", "Roxygen": "list(markdown = TRUE)", "SystemRequirements": "GNU make", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "cmake libglpk-dev make libxml2-dev", "Repository": "https://pgajer.r-universe.dev", "Date/Publication": "2026-10-08 19:38:27 UTC", "RemoteUrl": "https://github.com/pgajer/geosmooth", "RemoteRef": "HEAD", "RemoteSha": "5e8959743d1634b2d7369dd213eb18ebce646a74", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-08 22:36:25 UTC", "User": "root" }, "Author": "Pawel Gajer [aut, cre],\nGael Guennebaud [ctb] (Eigen),\nBenoit Jacob [ctb] (Eigen),\nAuthors of Eigen [cph] (Authorship and copyright in the included Eigen\nlibrary),\nYixuan Qiu [ctb, cph] (Spectra),\nContributors to Spectra [cph] (Copyright in the included Spectra\nlibrary),\nSunil Arya [ctb, cph] (ANN),\nDavid M. Mount [ctb, cph] (ANN),\nUniversity of Maryland [cph] (ANN)", "Maintainer": "Pawel Gajer ", "_user": "pgajer", "_type": "src", "_file": "geosmooth_0.2.0.tar.gz", "_fileid": "https://r2.ropensci.org/b020bba5af9154d8efe3a31b38594d1ab7edee7523ddd22d0c6daf4b797e6352", "_filesize": 3478146, "_sha256": "b020bba5af9154d8efe3a31b38594d1ab7edee7523ddd22d0c6daf4b797e6352", "_expires": "2027-01-16T23:07:37.000Z", "_created": "2026-10-08T22:36:25.000Z", "_published": "2026-10-08T23:07:41.330Z", "_buildurl": "https://github.com/r-universe/pgajer/actions/runs/37853948892", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 113575988390, "time": 363, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "11583089494" }, { "job": 113575988368, "time": 442, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11583727289" }, { "job": 113575988480, "time": 370, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11583791654" 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] }, { "page": "fit.metric.graph.lowpass", "title": "Fit Metric-Conductance Graph Low-Pass Regression", "topics": [ "fit.metric.graph.lowpass" ] }, { "page": "fit.ps.lps", "title": "Fit Prediction-Synchronized Local Polynomial Smoothing", "topics": [ "fit.ps.lps" ] }, { "page": "fit.pttf.trend.filtering", "title": "Fit A Parallel-Transport Trend Filter From A PTTF Operator", "topics": [ "fit.pttf.trend.filtering" ] }, { "page": "fit.slpl.tf", "title": "Fit A Fixed-Operator Synchronized LPL-TF Model", "topics": [ "fit.slpl.tf" ] }, { "page": "fit.ssrhe.hessian.l1.regression", "title": "Fit SSRHE-Style Hessian L1 Regression", "topics": [ "fit.ssrhe.hessian.l1.regression" ] }, { "page": "fit.ssrhe.hessian.regression", "title": "Fit SSRHE-Style Hessian-Energy Regression", "topics": [ "fit.ssrhe.hessian.regression", "fit.ssrhe.hessian.regression.cv", "fit.ssrhe.hessian.regression.gcv" ] }, { "page": "fit.subject.od", "title": "Fit Subject-Occupation Density", "topics": [ "fit.subject.od" ] }, { "page": "get.region.boundary", "title": "Get Boundary Vertices of a Region in a Graph", "topics": [ "get.region.boundary" ] }, { "page": "graph.trend.filtering.operator", "title": "Construct a Graph Trend-Filtering Operator", "topics": [ "graph.trend.filtering.operator" ] }, { "page": "harmonic.smoother", "title": "Perform Harmonic Smoothing with Topology Tracking", "topics": [ "harmonic.smoother" ] }, { "page": "lpl.tf.operator", "title": "Construct a Local Polynomial Lifting Trend-Filtering Operator", "topics": [ "lpl.tf.operator" ] }, { "page": "lps.backend.diagnostics", "title": "Report LPS Backend and Chart-Dimension Diagnostics", "topics": [ "lps.backend.diagnostics" ] }, { "page": "lps.grouped.foldid", "title": "Build a Cluster-Respecting Fold Assignment", "topics": [ "lps.grouped.foldid" ] }, { "page": "lps.nested.cv", "title": "Nested Cross-Validation for LPS with Explicit, Recorded Folds", "topics": [ "lps.nested.cv" ] }, { "page": "lps.pointwise.band", "title": "Pointwise Variance and Confidence Band for a Fixed-Configuration LPS Fit", "topics": [ "lps.pointwise.band" ] }, { "page": "malps.gcv", "title": "Compute MALPS Linear-Smoother Diagnostics", "topics": [ "malps.gcv" ] }, { "page": "materialize.synthetic", "title": "Materialize a synthetic-data specification", "topics": [ "materialize.synthetic" ] }, { "page": "materialize.synthetic.instance", "title": "Materialize a frozen synthetic instance", "topics": [ "materialize.synthetic.instance" ] }, { "page": "metric.graph.heat.eta.grid", "title": "Construct a Graph Heat-Time Grid", "topics": [ "metric.graph.heat.eta.grid" ] }, { "page": "metric.graph.heat.extend.lower", "title": "Propose a Guarded Lower Graph Heat Time", "topics": [ "metric.graph.heat.extend.lower" ] }, { "page": "metric.graph.lowpass.basis", "title": "Construct a Reusable Metric Graph Low-Pass Spectral Basis", "topics": [ "metric.graph.lowpass.basis" ] }, { "page": "metric.graph.lowpass.operator", "title": "Construct a 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"fitted.lpl_tf", "fitted.lps", "fitted.malps", "fitted.metric.graph.lowpass.fit", "fitted.metric.graph.lowpass.refit", "fitted.ps_lps", "fitted.pttf.trend.filtering.fit", "fitted.slpl_tf", "fitted.ssrhe.hessian.fit", "fitted.ssrhe.hessian.l1.fit", "fitted.ssrhe.hessian.refit", "model-values", "residuals.chart_kernel", "residuals.graph.trend.filtering.fit", "residuals.local_likelihood", "residuals.lpl_tf", "residuals.lps", "residuals.malps", "residuals.metric.graph.lowpass.fit", "residuals.metric.graph.lowpass.refit", "residuals.ps_lps", "residuals.pttf.trend.filtering.fit", "residuals.slpl_tf", "residuals.ssrhe.hessian.fit", "residuals.ssrhe.hessian.l1.fit", "residuals.ssrhe.hessian.refit" ] }, { "page": "normalize.density", "title": "Normalize Fitted Values Into a Density", "topics": [ "normalize.density", "normalize.density.default", "normalize.density.lps", "normalize.density.metric.graph.lowpass.fit", "normalize.density.metric.graph.lowpass.refit", "normalize.density.numeric", "normalize.density.ps_lps" ] }, { "page": "perform.harmonic.smoothing", "title": "Perform Harmonic Smoothing on Graph Function Values", "topics": [ "perform.harmonic.smoothing" ] }, { "page": "plot.harmonic_smoother", "title": "Plot Method for Harmonic Smoother Results", "topics": [ "plot.harmonic_smoother" ] }, { "page": "plot.synthetic_dataset", "title": "Plot a canonical synthetic dataset", "topics": [ "plot.synthetic_dataset" ] }, { "page": "predict.lpl_tf", "title": "Predict From A Local Polynomial Lifting Trend Filter", "topics": [ "predict.lpl_tf" ] }, { "page": "predict.lps", "title": "Predict from an LPS Fit", "topics": [ "predict.lps" ] }, { "page": "predict.malps", "title": "Predict From A MALPS Fit", "topics": [ "predict.malps" ] }, { "page": "predict.slpl_tf", "title": "Predict From A Fixed-Operator Synchronized LPL-TF Model", "topics": [ "predict.slpl_tf" ] }, { "page": "geosmooth-print-methods", "title": "Print Geosmooth S3 Objects", "topics": [ "print.graph.trend.filtering.fit", "print.lpl_tf_operator", "print.lps", "print.malps", "print.malps_bootstrap", "print.metric.graph.lowpass.fit", "print.metric.graph.lowpass.refit", "print.pttf.trend.filtering.fit", "print.pttf_operator", "print.slpl_tf_operator", "print.ssrhe.hessian.cv.fit", "print.ssrhe.hessian.fit", "print.ssrhe.hessian.gcv.fit", "print.ssrhe.hessian.l1.fit", "print.ssrhe.hessian.operator", "print.ssrhe.hessian.refit", "print.transported.graph.hessian.operator" ] }, { "page": "print.harmonic_smoother", "title": "Print Method for Harmonic Smoother Results", "topics": [ "print.harmonic_smoother" ] }, { "page": "print.summary.harmonic_smoother", "title": "Print Method for Summary of Harmonic Smoother Results", "topics": [ "print.summary.harmonic_smoother" ] }, { "page": "print.synthetic_dataset", "title": "Print a canonical synthetic dataset", "topics": [ "print.synthetic_dataset" ] }, { "page": "pttf.geometry", "title": "Construct PTTF Local Geometry", "topics": [ "pttf.geometry" ] }, { "page": "pttf.operator", "title": "Construct PTTF Transported-Difference Operators", "topics": [ "pttf.operator" ] }, { "page": "pttf.operator.filter.rows", "title": "Filter Rows Of A PTTF Operator", "topics": [ "pttf.operator.filter.rows" ] }, { "page": "refit", "title": "Refit A Model Using Its Stored Geometry", "topics": [ "refit", "refit.default" ] }, { "page": "refit.lpl_tf", "title": "Refit A Local Polynomial Lifting Trend Filter", "topics": [ "refit.lpl.tf", "refit.lpl_tf" ] }, { "page": "refit.malps", "title": "Refit A MALPS Smoother With A New Response", "topics": [ "refit.malps" ] }, { "page": "refit.metric.graph.lowpass.fit", "title": "Refit Metric-Conductance Graph Low-Pass Regression", "topics": [ "refit.metric.graph.lowpass", "refit.metric.graph.lowpass.fit" ] }, { "page": "refit.slpl_tf", "title": "Refit A Fixed-Operator Synchronized LPL-TF Model", "topics": [ "refit.slpl.tf", "refit.slpl_tf" ] }, { "page": "refit.ssrhe.hessian.fit", "title": 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