Package index • geosmooth

Shared model operations

refit(): Refit A Model Using Its Stored Geometry
smoother.matrix(): Extract A Conditional Smoother Matrix
fitted(<lps>) residuals(<lps>) fitted(<malps>) residuals(<malps>) fitted(<ps_lps>) residuals(<ps_lps>) fitted(<chart_kernel>) residuals(<chart_kernel>) fitted(<local_likelihood>) residuals(<local_likelihood>) fitted(<lpl_tf>) residuals(<lpl_tf>) fitted(<slpl_tf>) residuals(<slpl_tf>) fitted(<metric.graph.lowpass.fit>) residuals(<metric.graph.lowpass.fit>) fitted(<metric.graph.lowpass.refit>) residuals(<metric.graph.lowpass.refit>) fitted(<ssrhe.hessian.fit>) residuals(<ssrhe.hessian.fit>) fitted(<ssrhe.hessian.refit>) residuals(<ssrhe.hessian.refit>) fitted(<ssrhe.hessian.l1.fit>) residuals(<ssrhe.hessian.l1.fit>) fitted(<graph.trend.filtering.fit>) residuals(<graph.trend.filtering.fit>) fitted(<pttf.trend.filtering.fit>) residuals(<pttf.trend.filtering.fit>) fitted(<harmonic_smoother>): Extract Stored Fitted Values and Response Residuals
print(<summary.geosmooth_fit>) summary(<lps>) summary(<malps>) summary(<ps_lps>) summary(<chart_kernel>) summary(<local_likelihood>) summary(<lpl_tf>) summary(<slpl_tf>) summary(<metric.graph.lowpass.fit>) summary(<metric.graph.lowpass.refit>) summary(<ssrhe.hessian.fit>) summary(<ssrhe.hessian.refit>) summary(<ssrhe.hessian.l1.fit>) summary(<graph.trend.filtering.fit>) summary(<pttf.trend.filtering.fit>) summary(<density_fit>) print(<lpl_tf>) print(<slpl_tf>) print(<density_fit>) print(<ps_lps>) print(<chart_kernel>) print(<local_likelihood>): Summarize a Geometric Smoother or Probability-Mass Fit

Local regression and prediction synchronization

fit.lps(): Fit a Local Polynomial Smoother
fit.malps(): Fit Model-Averaged Local Polynomial Smoothing
fit.ps.lps(): Fit Prediction-Synchronized Local Polynomial Smoothing
fit.chart.kernel(): Fit a Chart-Kernel Smoother
fit.local.likelihood(): Fit a Local-Likelihood Density or Bernoulli Smoother

Model assessment and uncertainty

lps.grouped.foldid(): Build a Cluster-Respecting Fold Assignment
lps.nested.cv(): Nested Cross-Validation for LPS with Explicit, Recorded Folds
lps.backend.diagnostics(): Report LPS Backend and Chart-Dimension Diagnostics
lps.pointwise.band(): Pointwise Variance and Confidence Band for a Fixed-Configuration LPS Fit
malps.gcv(): Compute MALPS Linear-Smoother Diagnostics
bootstrap.malps(): Bootstrap Uncertainty For A Fixed-Selection MALPS Fit

Lifting trend filters

lpl.tf.operator(): Construct a Local Polynomial Lifting Trend-Filtering Operator
fit.lpl.tf(): Fit A Local Polynomial Lifting Trend Filter
slpl.tf.operator(): Construct A Synchronized LPL-TF Fixed Operator
fit.slpl.tf(): Fit A Fixed-Operator Synchronized LPL-TF Model

Graph spectral smoothing and graph trend filtering

fit.metric.graph.lowpass(): Fit Metric-Conductance Graph Low-Pass Regression
metric.graph.lowpass.operator(): Construct a Metric-Conductance Graph Low-Pass Operator
metric.graph.lowpass.basis(): Construct a Reusable Metric Graph Low-Pass Spectral Basis
apply.metric.graph.lowpass.path(): Apply a Metric Graph Low-Pass Filter Path
metric.graph.heat.eta.grid(): Construct a Graph Heat-Time Grid
metric.graph.heat.extend.lower(): Propose a Guarded Lower Graph Heat Time
graph.trend.filtering.operator(): Construct a Graph Trend-Filtering Operator
fit.graph.trend.filtering(): Fit Graph Trend Filtering

Hessian-energy regression

ssrhe.hessian.operator(): Construct an SSRHE-Style Local Hessian Energy Operator
fit.ssrhe.hessian.regression(): Fit SSRHE-Style Hessian-Energy Regression
fit.ssrhe.hessian.l1.regression(): Fit SSRHE-Style Hessian L1 Regression
ssrhe.support.grid(): Build Candidate Support Profiles for SSRHE Adaptive-Radius Tuning

Harmonic interpolation

perform.harmonic.smoothing(): Perform Harmonic Smoothing on Graph Function Values
harmonic.smoother(): Perform Harmonic Smoothing with Topology Tracking
get.region.boundary(): Get Boundary Vertices of a Region in a Graph

Probability mass and subject occupation

fit.density(): Fit a Density Estimator
normalize.density(): Normalize Fitted Values Into a Density
fit.subject.od(): Fit Subject-Occupation Density

Experimental transport methods

pttf.geometry(): Construct PTTF Local Geometry
pttf.operator(): Construct PTTF Transported-Difference Operators
pttf.operator.filter.rows(): Filter Rows Of A PTTF Operator
fit.pttf.trend.filtering(): Fit A Parallel-Transport Trend Filter From A PTTF Operator
transported.graph.hessian.operator(): Construct a Transported Graph Hessian Operator

Synthetic datasets: specifications, responses, and reproducibility

synthetic.spec(): Assemble a synthetic-data specification
materialize.synthetic(): Materialize a synthetic-data specification
materialize.synthetic.instance(): Materialize a frozen synthetic instance
synthetic.registry.ids(): List maintained synthetic recipe IDs
synthetic.registry.spec(): Resolve a maintained synthetic recipe
synthetic.registry.seed(): Resolve the legacy seed for an SSRHE registry recipe
validate.synthetic.dataset(): Validate a canonical synthetic dataset
compare.synthetic.dataset(): Compare two synthetic datasets scientifically
synthetic.dataset.checksum(): Compute the canonical content checksum of a synthetic dataset
synthetic.sampling.stratified(): Legacy G4 stratified sampling specification
synthetic.truth.named(): Specify a versioned named truth evaluator
synthetic.truth.polynomial(): Specify a polynomial truth
synthetic.truth.gaussian.mixture(): Specify a Gaussian-mixture truth
synthetic.truth.occupation.mixture(): Specify a finite-design occupation-mixture truth
synthetic.truth.logit(): Specify a sinusoidal finite-design logit truth
synthetic.response.gaussian(): Gaussian response specification
synthetic.response.heteroskedastic.gaussian(): Heteroskedastic Gaussian response specification
synthetic.response.clustered.gaussian(): Clustered Gaussian response specification
synthetic.response.laplace.outlier(): Laplace response with Gaussian contamination
synthetic.response.bernoulli(): Bernoulli response specification

Package overview

geosmooth geosmooth-package: geosmooth package

S3 methods

Methods selected by the corresponding generic and fitted-object class.

predict(<lpl_tf>): Predict From A Local Polynomial Lifting Trend Filter
predict(<lps>): Predict from an LPS Fit
predict(<malps>): Predict From A MALPS Fit
predict(<slpl_tf>): Predict From A Fixed-Operator Synchronized LPL-TF Model
refit(): Refit A Model Using Its Stored Geometry
refit(<lpl_tf>): Refit A Local Polynomial Lifting Trend Filter
refit(<malps>): Refit A MALPS Smoother With A New Response
refit(<metric.graph.lowpass.fit>): Refit Metric-Conductance Graph Low-Pass Regression
refit(<slpl_tf>): Refit A Fixed-Operator Synchronized LPL-TF Model
refit(<ssrhe.hessian.fit>): Refit SSRHE-Style Hessian-Energy Regression
refit(<ssrhe.hessian.l1.fit>): Refit SSRHE-Style Hessian L1 Regression
smoother.matrix(): Extract A Conditional Smoother Matrix
smoother.matrix(<lps>): Extract the Linear-Smoother Matrix of a Fixed-Configuration LPS Fit
smoother.matrix(<malps>): Construct The Conditional MALPS Smoother Matrix
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.operator>) print(<ssrhe.hessian.l1.fit>) print(<ssrhe.hessian.fit>) print(<ssrhe.hessian.refit>) print(<ssrhe.hessian.cv.fit>) print(<ssrhe.hessian.gcv.fit>) print(<transported.graph.hessian.operator>): Print Geosmooth S3 Objects
print(<summary.geosmooth_fit>) summary(<lps>) summary(<malps>) summary(<ps_lps>) summary(<chart_kernel>) summary(<local_likelihood>) summary(<lpl_tf>) summary(<slpl_tf>) summary(<metric.graph.lowpass.fit>) summary(<metric.graph.lowpass.refit>) summary(<ssrhe.hessian.fit>) summary(<ssrhe.hessian.refit>) summary(<ssrhe.hessian.l1.fit>) summary(<graph.trend.filtering.fit>) summary(<pttf.trend.filtering.fit>) summary(<density_fit>) print(<lpl_tf>) print(<slpl_tf>) print(<density_fit>) print(<ps_lps>) print(<chart_kernel>) print(<local_likelihood>): Summarize a Geometric Smoother or Probability-Mass Fit
print(<harmonic_smoother>): Print Method for Harmonic Smoother Results
print(<summary.harmonic_smoother>): Print Method for Summary of Harmonic Smoother Results
print(<synthetic_dataset>): Print a canonical synthetic dataset
summary(<harmonic_smoother>): Summary Method for Harmonic Smoother Results
plot(<harmonic_smoother>): Plot Method for Harmonic Smoother Results
plot(<synthetic_dataset>): Plot a canonical synthetic dataset
as.data.frame(<synthetic_dataset>): Summarize a synthetic dataset as one metadata row
normalize.density(): Normalize Fitted Values Into a Density