This package is considered a duplicate. The official version of this package is found at:https://pgajer.r-universe.dev/geosmooth

Package: geosmooth 0.1.0

Pawel Gajer

geosmooth: Geometric Smoothing and Conditional Expectation Methods

Provides geometric methods for nonparametric regression and density estimation on data represented as coordinate matrices or weighted graphs. Methods include local polynomial smoothing, model-averaged local polynomial smoothing, local polynomial lifting trend filtering, synchronized local polynomial lifting trend filtering, graph low-pass filtering, and Hessian-energy regression. Methodological references include Gajer and Ravel (2025) "Adaptive Geometric Regression for High-Dimensional Structured Data" <doi:10.48550/arXiv.2511.03817>, Fan and Gijbels (1996, ISBN:9780412983214), Wang et al. (2016) "Trend Filtering on Graphs" <https://www.jmlr.org/papers/v17/15-147.html>, and Kim et al. (2009) "Semi-Supervised Regression Using Hessian Energy" <https://papers.nips.cc/paper/3741-semi-supervised-regression-using-hessian-energy-with-an-application-to-semi-supervised-dimensionality-reduction>.

Authors:Pawel Gajer [aut, cre], Gael Guennebaud [ctb], Benoit Jacob [ctb], Authors of Eigen [cph], Yixuan Qiu [ctb, cph], Contributors to Spectra [cph], Sunil Arya [ctb, cph], David M. Mount [ctb, cph], University of Maryland [cph]

geosmooth_0.1.0.tar.gz
geosmooth_0.1.0.zip(r-4.7-x86_64)geosmooth_0.1.0.zip(r-4.7-arm64)geosmooth_0.1.0.zip(r-4.6-x86_64)geosmooth_0.1.0.zip(r-4.6-arm64)
geosmooth_0.1.0.tar.gz(r-4.7-arm64)geosmooth_0.1.0.tar.gz(r-4.7-x86_64)geosmooth_0.1.0.tar.gz(r-4.6-arm64)geosmooth_0.1.0.tar.gz(r-4.6-x86_64)
geosmooth_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
geosmooth/json (API)

# Install 'geosmooth' in R:

install.packages('geosmooth', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pgajer/geosmooth/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

openblas cpp

2.70 score 569 downloads 93 exports 18 dependencies

Last updated from:220651914f. Checks:10 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	359
linux-devel-x86_64	OK	320
source / vignettes	OK	369
linux-release-arm64	OK	358
linux-release-x86_64	OK	418
windows-devel-arm64	OK	557
windows-devel-x86_64	OK	559
windows-release-arm64	OK	557
windows-release-x86_64	OK	507
wasm-release	OK	184

Dependencies:cli cpp11 dgraphs digest FNN glue igraph jsonlite lattice lifecycle magrittr MASS Matrix pkgconfig Rcpp RcppEigen rlang vctrs

Synthetic datasets for geometric smoothing

Rendered fromsynthetic-datasets.Rmdusingknitr::rmarkdown

Last update: 2026-09-12
Started: 2026-09-12

Citation

Readme and manuals

Help Manual

Help page	Topics
geosmooth package	geosmooth-package geosmooth
Apply a Metric Graph Low-Pass Filter Path	apply.metric.graph.lowpass.path
Summarize a synthetic dataset as one metadata row	as.data.frame.synthetic_dataset
Bootstrap Uncertainty For A Fixed-Selection MALPS Fit	bootstrap.malps
Compare two synthetic datasets scientifically	compare.synthetic.dataset
Exact latent-segment lengths under a synthetic geometry	edge.lengths.synthetic.geometry
Embed latent coordinates through a synthetic geometry	embed.synthetic.geometry
Fit a Chart-Kernel Smoother	fit.chart.kernel
Fit a Density Estimator	fit.density
Fit Graph Trend Filtering	fit.graph.trend.filtering
Fit a Local-Likelihood Density or Bernoulli Smoother	fit.local.likelihood
Fit A Local Polynomial Lifting Trend Filter	fit.lpl.tf
Fit a Local Polynomial Smoother	fit.lps
Fit Model-Averaged Local Polynomial Smoothing	fit.malps
Fit Metric-Conductance Graph Low-Pass Regression	fit.metric.graph.lowpass
Fit Prediction-Synchronized Local Polynomial Smoothing	fit.ps.lps
Fit A Parallel-Transport Trend Filter From A PTTF Operator	fit.pttf.trend.filtering
Fit A Fixed-Operator Synchronized LPL-TF Model	fit.slpl.tf
Fit SSRHE-Style Hessian L1 Regression	fit.ssrhe.hessian.l1.regression
Fit SSRHE-Style Hessian-Energy Regression	fit.ssrhe.hessian.regression
Select SSRHE Hessian Regression Penalties by Label Cross-Validation	fit.ssrhe.hessian.regression.cv
Select SSRHE Hessian Regression Penalties by GCV	fit.ssrhe.hessian.regression.gcv
Fit Subject-Occupation Density	fit.subject.od
Get Boundary Vertices of a Region in a Graph	get.region.boundary
Construct a Graph Trend-Filtering Operator	graph.trend.filtering.operator
Perform Harmonic Smoothing with Topology Tracking	harmonic.smoother
Construct a Local Polynomial Lifting Trend-Filtering Operator	lpl.tf.operator
Report LPS Backend and Chart-Dimension Diagnostics	lps.backend.diagnostics
Build a Cluster-Respecting Fold Assignment	lps.grouped.foldid
Nested Cross-Validation for LPS with Explicit, Recorded Folds	lps.nested.cv
Pointwise Variance and Confidence Band for a Fixed-Configuration LPS Fit	lps.pointwise.band
Extract the Linear-Smoother Matrix of a Fixed-Configuration LPS Fit	lps.smoother.matrix
Compute MALPS Linear-Smoother Diagnostics	malps.gcv
Construct The Conditional MALPS Smoother Matrix	malps.smoother.matrix
Materialize a synthetic-data specification	materialize.synthetic
Materialize a frozen synthetic instance	materialize.synthetic.instance
Construct a Graph Heat-Time Grid	metric.graph.heat.eta.grid
Propose a Guarded Lower Graph Heat Time	metric.graph.heat.extend.lower
Construct a Reusable Metric Graph Low-Pass Spectral Basis	metric.graph.lowpass.basis
Construct a Metric-Conductance Graph Low-Pass Operator	metric.graph.lowpass.operator
Normalize Fitted Values Into a Density	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
Perform Harmonic Smoothing on Graph Function Values	perform.harmonic.smoothing
Plot Method for Harmonic Smoother Results	plot.harmonic_smoother
Plot a canonical synthetic dataset	plot.synthetic_dataset
Predict From A Local Polynomial Lifting Trend Filter	predict.lpl_tf
Predict from an LPS Fit	predict.lps
Predict From A MALPS Fit	predict.malps
Predict From A Fixed-Operator Synchronized LPL-TF Model	predict.slpl_tf
Print Method for Harmonic Smoother Results	print.harmonic_smoother
Print Method for Summary of Harmonic Smoother Results	print.summary.harmonic_smoother
Print a canonical synthetic dataset	print.synthetic_dataset
Construct PTTF Local Geometry	pttf.geometry
Construct PTTF Transported-Difference Operators	pttf.operator
Filter Rows Of A PTTF Operator	pttf.operator.filter.rows
Evaluate gradients of all quadforms	quadform.gradient
Evaluate the induced metric of a quadform geometry	quadform.metric
Refit A Local Polynomial Lifting Trend Filter	refit.lpl.tf
Refit A MALPS Smoother With A New Response	refit.malps
Refit Metric-Conductance Graph Low-Pass Regression	refit.metric.graph.lowpass
Refit A Fixed-Operator Synchronized LPL-TF Model	refit.slpl.tf
Refit SSRHE-Style Hessian L1 Regression	refit.ssrhe.hessian.l1.regression
Refit SSRHE-Style Hessian-Energy Regression	refit.ssrhe.hessian.regression
Construct A Synchronized LPL-TF Fixed Operator	slpl.tf.operator
Construct an SSRHE-Style Local Hessian Energy Operator	ssrhe.hessian.operator
Build Candidate Support Profiles for SSRHE Adaptive-Radius Tuning	ssrhe.support.grid
Summary Method for Harmonic Smoother Results	summary.harmonic_smoother
Specify a circle geometry	synthetic.circle
Compute the canonical content checksum of a synthetic dataset	synthetic.dataset.checksum
Specify a helix geometry	synthetic.helix
Specify a point-line junction	synthetic.point.line.junction
Specify a quadform geometry	synthetic.quadform
List maintained synthetic recipe IDs	synthetic.registry.ids
Resolve the legacy seed for an SSRHE registry recipe	synthetic.registry.seed
Resolve a maintained synthetic recipe	synthetic.registry.spec
Bernoulli response specification	synthetic.response.bernoulli
Clustered Gaussian response specification	synthetic.response.clustered.gaussian
Gaussian response specification	synthetic.response.gaussian
Heteroskedastic Gaussian response specification	synthetic.response.heteroskedastic.gaussian
Laplace response with Gaussian contamination	synthetic.response.laplace.outlier
Clustered sampling specification	synthetic.sampling.clustered
Dirichlet sampling with structural zeros	synthetic.sampling.dirichlet.zeros
Gapped-uniform sampling specification	synthetic.sampling.gapped.uniform
Deterministic interval-grid sampling specification	synthetic.sampling.grid.interval
Legacy G4 stratified sampling specification	synthetic.sampling.stratified
Truncated-normal sampling specification	synthetic.sampling.truncated.normal
Uniform-box sampling specification	synthetic.sampling.uniform.box
Uniform-disk sampling specification	synthetic.sampling.uniform.disk
Uniform-interval sampling specification	synthetic.sampling.uniform.interval
Uniform-rectangle sampling specification	synthetic.sampling.uniform.rectangle
Specify a simplex geometry	synthetic.simplex
Assemble a synthetic-data specification	synthetic.spec
Specify a sphere-cap geometry	synthetic.sphere.cap
Assemble a stratified geometry	synthetic.stratified
Specify a point stratum	synthetic.stratum.point
Specify a rectangle stratum	synthetic.stratum.rectangle
Specify a line-segment stratum	synthetic.stratum.segment
Specify a torus-patch geometry	synthetic.torus.patch
Specify a trefoil-knot geometry	synthetic.trefoil
Specify a Gaussian-mixture truth	synthetic.truth.gaussian.mixture
Specify a sinusoidal finite-design logit truth	synthetic.truth.logit
Specify a versioned named truth evaluator	synthetic.truth.named
Specify a finite-design occupation-mixture truth	synthetic.truth.occupation.mixture
Specify a polynomial truth	synthetic.truth.polynomial
Construct a Transported Graph Hessian Operator	transported.graph.hessian.operator
Validate a canonical synthetic dataset	validate.synthetic.dataset

Package: geosmooth 0.1.0

geosmooth: Geometric Smoothing and Conditional Expectation Methods

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)