Package index • fdars

Functional Data Objects

fdata(): Create a functional data object
fdata.cen(): Center functional data
deriv(): Compute functional derivative
fdata.gradient(): High-Accuracy Gradient for Functional Data
mean(<fdata>): Compute functional mean
median(): Compute Functional Median
trimmed(): Compute Functional Trimmed Mean
trimvar(): Compute Functional Trimmed Variance
var(): Functional Variance
sd(): Functional Standard Deviation
normalize(): Normalize functional data
standardize(): Standardize functional data (z-score normalization)
scale_minmax(): Min-Max scaling for functional data
gmed(): Geometric Median of Functional Data
inprod.fdata(): Inner Product of Functional Data
int.simpson(): Utility Functions for Functional Data Analysis
localavg.fdata(): Local Averages Feature Extraction
fdata.bootstrap(): Bootstrap Functional Data
fdata.bootstrap.ci(): Bootstrap Confidence Intervals for Functional Statistics
df_to_fdata2d(): Convert DataFrame to 2D functional data

Basis Representation

fdata2basis(): Convert Functional Data to Basis Coefficients
fdata2basis_2d(): Convert 2D Functional Data to Tensor Product Basis Coefficients
fdata2basis_cv(): Cross-Validation for Basis Function Number Selection
basis2fdata(): Basis Representation Functions for Functional Data
basis2fdata_2d(): Reconstruct 2D Functional Data from Tensor Product Basis Coefficients
fdata2fd(): Convert Functional Data to fd class
fdata2pc(): Convert Functional Data to Principal Component Scores
fdata2pls(): Convert Functional Data to PLS Scores
basis.aic(): AIC for Basis Representation
basis.bic(): BIC for Basis Representation
basis.gcv(): GCV Score for Basis Representation
select.basis.auto(): Automatic Per-Curve Basis Type and Number Selection
pspline(): P-spline Smoothing for Functional Data
pspline.2d(): P-spline Smoothing for 2D Functional Data

Andrews Transformation

andrews_transform(): Andrews Transformation
andrews_loadings(): Andrews Loadings: Project FPCA Eigenfunctions to Original Variables

Elastic Alignment

srsf.transform(): Elastic Alignment for Functional Data
srsf.inverse(): Inverse SRSF Transform
elastic.align(): Elastic Curve Alignment
elastic.distance(): Elastic Distance Matrix
metric.elastic(): Elastic Distance (Metric Dispatcher Alias)
karcher.mean(): Karcher Mean in Elastic Metric
periodic.rotate(): Periodic Rotation for Functional Data
alignment.quality(): Alignment Quality Diagnostics
elastic.decomposition(): Elastic Phase-Amplitude Decomposition
amplitude.distance(): Amplitude Distance Matrix
phase.distance(): Phase Distance Matrix
elastic.align.constrained(): Landmark-Constrained Elastic Alignment
alignment.pairwise.consistency(): Pairwise Alignment Consistency
elastic.pair(): Elastic Pairwise Alignment
srsf.reparameterize(): Apply Warping Function to a Curve
warp.complexity(): Warping Complexity
warp.compose(): Compose Two Warping Functions
warp.smoothness(): Warping Smoothness
plot(<elastic.align>): Plot Elastic Alignment Results
plot(<karcher.mean>): Plot Karcher Mean Results
plot(<alignment.quality>): Plot Alignment Quality Diagnostics

Advanced Alignment

karcher.median(): Karcher Median in Elastic Metric
robust.karcher.mean(): Trimmed Karcher Mean in Elastic Metric
elastic.depth(): Elastic Depth for Functional Data
elastic.outlier.detection(): Elastic Outlier Detection
shape.confidence.interval(): Bootstrap Confidence Bands for Elastic Karcher Mean
bayesian.align.pair(): Bayesian Pairwise Curve Alignment
elastic.align.pair.multires(): Multi-Resolution Pairwise Curve Alignment
elastic.align.pair.closed(): Elastic Alignment of Closed Curves
elastic.distance.closed(): Elastic Distance Between Closed Curves
karcher.mean.closed(): Karcher Mean for Closed Curves
elastic.partial.match(): Elastic Partial Curve Matching
curve.geodesic(): Geodesic Path Between Two Curves
peak.persistence(): Peak Persistence Analysis
transfer.alignment(): Transfer Alignment Between Functional Samples
gauss.model(): Gaussian Generative Model for Elastic Functional Data
joint.gauss.model(): Joint Amplitude-Phase Gaussian Generative Model
horiz.fpns(): Horizontal Functional Principal Nested Spheres

Landmark Registration

detect.landmarks(): Landmark Registration for Functional Data
landmark.register(): Landmark Registration
plot(<landmark.register>): Plot Landmark Registration Results

TSRVF

tsrvf.transform(): TSRVF: Transported Square-Root Velocity Function
tsrvf.from.alignment(): TSRVF from Pre-computed Alignment
tsrvf.inverse(): Inverse TSRVF Transform
plot(<tsrvf>): Plot TSRVF Results

Tolerance Bands

tolerance.band(): Tolerance Bands for Functional Data
plot(<tolerance.band>): Plot Tolerance Band

Depth Functions

depth(): Depth Functions for Functional Data
depth.BD(): Band Depth
depth.FM(): Fraiman-Muniz Depth
depth.FSD(): Functional Spatial Depth
depth.KFSD(): Kernel Functional Spatial Depth
depth.MBD(): Modified Band Depth
depth.MEI(): Modified Epigraph Index
depth.mode(): Modal Depth
depth.RP(): Random Projection Depth
rp.stat(): Random Projection Statistic
depth.RPD(): Random Projection Depth with Derivatives
depth.RT(): Random Tukey Depth
streaming.depth(): Streaming Depth for Functional Data
depth.streaming(): Streaming Depth (Alias)

Distance & Metrics

metric(): Distance Metrics for Functional Data
metric.DTW(): Dynamic Time Warping for Functional Data
metric.hausdorff(): Hausdorff Metric for Functional Data
metric.kl(): Kullback-Leibler Divergence Metric for Functional Data
metric.lp(): Lp Metric for Functional Data
metric.softDTW(): Soft Dynamic Time Warping Distance
softdtw.barycenter(): Soft-DTW Barycenter
norm(): Compute Lp Norm of Functional Data
semimetric.basis(): Semi-metric based on Basis Expansion
semimetric.deriv(): Semi-metric based on Derivatives
semimetric.fourier(): Semi-metric based on Fourier Coefficients (FFT)
semimetric.hshift(): Semi-metric based on Horizontal Shift (Time Warping)
semimetric.pca(): Semi-metric based on Principal Components
group.distance(): Compute Distance/Similarity Between Groups of Functional Data

Clustering

cluster.fcm(): Fuzzy C-Means Clustering for Functional Data
cluster.gmm(): Gaussian Mixture Model Clustering for Functional Data
gmm.em(): Gaussian Mixture Model EM on Feature Matrix
cluster.init(): K-Means++ Center Initialization
cluster.kmeans(): Clustering Functions for Functional Data
cluster.optim(): Optimal Number of Clusters for Functional K-Means

Outlier Detection

outliergram(): Outliergram for Functional Data
outliers.boxplot(): Outlier Detection using Functional Boxplot
outliers.depth.pond(): Outlier Detection for Functional Data
outliers.depth.trim(): Outlier Detection using Trimmed Depth
outliers.lrt(): LRT-based Outlier Detection for Functional Data
outliers.thres.lrt(): LRT Outlier Detection Threshold
outliers.lrt.dist(): LRT Outlier Threshold with Bootstrap Distribution
magnitudeshape(): Magnitude-Shape Outlier Detection for Functional Data

Regression

fregre.basis(): Functional Basis Regression
fregre.basis.cv(): Cross-Validation for Functional Basis Regression
fregre.lm(): Functional Linear Model (FPC-based)
fregre.lm.cv(): Cross-Validation for FPC Component Selection (fregre.lm)
fregre.bootstrap.ci(): Bootstrap Confidence Intervals for Functional Coefficient
fregre.np(): Nonparametric Functional Regression
fregre.np.cv(): Cross-Validation for Nonparametric Functional Regression
fregre.np.mixed(): Nonparametric Functional Regression with Mixed Predictors
fregre.np.multi(): Nonparametric Regression with Multiple Functional Predictors
fregre.pc(): Functional Regression
fregre.pc.cv(): Cross-Validation for Functional PC Regression
functional.logistic(): Functional Logistic Regression
predict(<fregre.logistic>): Predict from Functional Logistic Model
model.selection.ncomp(): Model Selection for Number of FPC Components
optim.np(): Optimize Bandwidth Using Cross-Validation
flm.test(): Statistical Tests for Functional Data
pred.MAE(): Mean Absolute Error
pred.MSE(): Mean Squared Error
pred.R2(): R-Squared (Coefficient of Determination)
pred.RMSE(): Root Mean Squared Error

Function-on-Scalar Regression

fosr(): Function-on-Scalar Regression
fosr.fpc(): FPC-based Function-on-Scalar Regression
fosr.2d(): 2D Function-on-Scalar Regression
fanova(): Functional ANOVA

Classification

fclassif(): Supervised Classification of Functional Data
fclassif.cv(): Cross-Validated Functional Classification

Cross-Validation

cv.fdata(): Unified K-Fold Cross-Validation for Functional Data
plot(<cv.fdata>): Plot Method for cv.fdata
print(<cv.fdata>): Print Method for cv.fdata

Functional Mixed Models

fmm(): Functional Mixed Models
fmm.predict(): Predict from Functional Mixed Model
fmm.test.fixed(): Permutation Test for Fixed Effects in FMM

Seasonal Analysis

estimate.period(): Estimate Seasonal Period using FFT
detect.period(): Seasonal Analysis Functions for Functional Data
detect.periods(): Detect Multiple Concurrent Periods
detect.peaks(): Detect Peaks in Functional Data
autoperiod(): Autoperiod: Hybrid FFT + ACF Period Detection
cfd.autoperiod(): CFDAutoperiod: Clustered Filtered Detrended Autoperiod
sazed(): SAZED: Spectral-ACF Zero-crossing Ensemble Detection
lomb.scargle(): Lomb-Scargle Periodogram
matrix.profile(): Matrix Profile for Motif Discovery and Period Detection
stl.fd(): STL Decomposition: Seasonal and Trend decomposition using LOESS
ssa.fd(): Singular Spectrum Analysis (SSA) for Time Series Decomposition
seasonal.strength(): Measure Seasonal Strength
seasonal.strength.curve(): Time-Varying Seasonal Strength
detect.seasonality.changes(): Detect Changes in Seasonality
detect.seasonality.changes.auto(): Detect Seasonality Changes with Automatic Threshold
detect_amplitude_modulation(): Detect Amplitude Modulation in Seasonal Time Series
instantaneous.period(): Estimate Instantaneous Period
analyze.peak.timing(): Analyze Peak Timing Variability
classify.seasonality(): Classify Seasonality Type
detrend(): Remove Trend from Functional Data
decompose(): Seasonal-Trend Decomposition

Penalized Basis Smoothing

smooth.basis.fd(): Penalized Basis Smoothing
smooth.basis.gcv(): Penalized Basis Smoothing with GCV-Optimal Lambda

Elastic FPCA

vert.fpca(): Elastic FPCA
horiz.fpca(): Horizontal (Phase) FPCA
joint.fpca(): Joint (Amplitude + Phase) FPCA

Elastic Regression

elastic.regression(): Elastic Regression
elastic.logistic(): Elastic Logistic Classification
elastic.pcr(): Elastic Principal Component Regression
predict(<elastic.regression>): Predict from Elastic Regression
predict(<elastic.logistic>): Predict from Elastic Logistic Classification

Scalar-on-Shape Regression

scalar.on.shape(): Scalar-on-Shape Regression
predict(<scalar.on.shape>): Predict from a Scalar-on-Shape Model
print(<scalar.on.shape>): Print Scalar-on-Shape Model

Robust Regression

fregre.l1(): L1 (Least Absolute Deviation) Functional Regression
fregre.huber(): Huber M-Estimation Functional Regression
predict(<fregre.robust>): Predict from Robust Functional Regression

Statistical Process Monitoring

spm: Statistical Process Monitoring for Functional Data
spm.phase1(): Build Univariate SPM Control Chart (Phase I)
spm.monitor(): Monitor New Functional Data (Phase II)
spm.ewma(): EWMA-Based SPM Monitoring
spm.contributions(): SPM Contribution Diagnostics
plot(<spm.chart>): Plot an SPM Phase I control chart
plot(<spm.monitor>): Plot SPM Phase II monitoring results
mfpca(): Multivariate Functional Principal Component Analysis
frcc.phase1(): Build Functional Regression Control Chart (Phase I)
frcc.monitor(): Monitor New Data Against FRCC Chart (Phase II)

Advanced SPM

spm.ncomp.select(): Select Number of Principal Components
spm.rules(): Evaluate Control Chart Rules
spm.limit.robust(): Robust Control Limits via Alternative Methods
spm.pc.contributions(): Per-PC Contributions to T-Squared Statistic
spm.arl(): Estimate Average Run Length (ARL)
spm.arl.ewma(): Estimate ARL for EWMA-T-Squared Chart
spm.cusum(): CUSUM Monitoring for Functional Data
plot(<spm.cusum>): Plot CUSUM monitoring results
spm.mewma(): MEWMA Monitoring for Functional Data
plot(<spm.mewma>): Plot MEWMA monitoring results
spm.amewma(): Adaptive MEWMA (AMEWMA) Monitoring for Functional Data
plot(<spm.amewma>): Plot AMEWMA monitoring results
spm.phase1.iterative(): Iterative Phase I Chart Construction

Profile & Partial Monitoring

spm.profile.phase1(): Profile Monitoring Phase I
spm.profile.monitor(): Monitor New Data Against Profile Chart (Phase II)
spm.monitor.partial(): Monitor a Partially-Observed Curve
spm.monitor.partial.batch(): Monitor a Batch of Partially-Observed Curves
spm.elastic.phase1(): Elastic SPM Phase I Chart
plot(<spm.elastic.chart>): Plot Elastic SPM Phase I Chart
spm.elastic.monitor(): Elastic SPM Phase II Monitoring

Elastic Clustering

elastic.kmeans(): Elastic Clustering for Functional Data
elastic.hclust(): Elastic Hierarchical Clustering
elastic.cutree(): Cut Elastic Dendrogram
plot(<elastic.kmeans>): Plot Elastic K-Means Result
plot(<elastic.hclust>): Plot Elastic Hierarchical Clustering Result
print(<elastic.kmeans>): Print Elastic K-Means Result
print(<elastic.hclust>): Print Elastic Hierarchical Clustering Result

Shape Analysis

shape.representative(): Shape Representative (Orbit Representative)
shape.distance(): Shape Distance Between Two Curves
shape.mean(): Shape Mean (Karcher Mean in Quotient Space)
shape.distance.matrix(): Shape Distance Matrix
plot(<shape.mean>): Plot Shape Mean
print(<shape.mean>): Print Shape Mean

Alignment Extensions

elastic.lambda.cv(): Cross-Validation for Elastic Alignment Regularization Parameter
warp.statistics(): Warping Function Statistics
phase.boxplot(): Phase Boxplot for Warping Functions
warp.inverse(): Invert a Warping Function
warp.inverse.error(): Warp Inverse Error
elastic.pair.penalized(): Penalized Elastic Pairwise Alignment
alignment.diagnostics(): Alignment Diagnostics
alignment.diagnostics.pairwise(): Pairwise Alignment Diagnostics
plot(<lambda.cv>): Plot Lambda CV Result
plot(<warp.statistics>): Plot Warp Statistics
plot(<phase.boxplot>): Plot Phase Boxplot
plot(<alignment.diagnostics>): Plot Alignment Diagnostics
print(<lambda.cv>): Print Lambda CV Result
print(<warp.statistics>): Print Warp Statistics
print(<phase.boxplot>): Print Phase Boxplot
print(<alignment.diagnostics>): Print Alignment Diagnostics

Elastic Changepoint

elastic.changepoint(): Elastic Changepoint Detection

Conformal Prediction

conformal.fregre.lm(): Conformal Prediction for Functional Linear Model
conformal.fregre.np(): Conformal Prediction for Nonparametric Functional Regression
conformal.elastic.regression(): Conformal Prediction for Elastic Regression
conformal.elastic.pcr(): Conformal Prediction for Elastic PCR
conformal.elastic.logistic(): Conformal Prediction for Elastic Logistic Regression
conformal.logistic(): Conformal Prediction for Logistic Regression
conformal.classif(): Conformal Classification
cv.conformal.regression(): Cross-Conformal (CV+) Regression
cv.conformal.classification(): Cross-Conformal (CV+) Classification
jackknife.plus(): Jackknife+ Regression
conformal.generic.regression(): Generic Conformal Regression
conformal.generic.classification(): Generic Conformal Classification

Explainability

fregre.pdp(): Functional Partial Dependence Plot
fregre.shap(): FPC SHAP Values
fregre.ale(): Accumulated Local Effects
fregre.lime(): LIME Explanation
fregre.anchor(): Anchor Explanation
fregre.counterfactual(): Counterfactual Explanation
fregre.saliency(): Functional Saliency Map
fregre.importance(): FPC Permutation Importance
fregre.conditional.importance(): Conditional Permutation Importance
fregre.influence(): Influence Diagnostics
fregre.vif(): Variance Inflation Factors
fregre.dfbetas(): DFBETAS and DFFITS
fregre.loo(): LOO-CV and PRESS
fregre.sobol(): Sobol Indices
fregre.friedman(): Friedman H-Statistic
fregre.conformal(): Conformal Prediction
fregre.stability(): Explanation Stability
fregre.depth(): Regression Depth
fregre.domain(): Domain Selection
fregre.prediction.interval(): Prediction Intervals
fregre.prototype(): Prototype and Criticism
fregre.beta.decomp(): Beta Decomposition
fregre.pointwise(): Pointwise Importance
fregre.significant.regions(): Significant Regions
fregre.calibration(): Calibration Diagnostics (Logistic)
fregre.ece(): Expected Calibration Error (Logistic)
elastic.attribution(): Elastic PCR Attribution

Smoothing

S.KNN(): K-Nearest Neighbors Smoother Matrix
S.LCR(): Local Cubic Regression Smoother Matrix
S.LLR(): Local Linear Regression Smoother Matrix
S.LPR(): Local Polynomial Regression Smoother Matrix
S.NW(): Smoothing Functions for Functional Data
CV.S(): Cross-Validation for Smoother Selection
GCV.S(): Generalized Cross-Validation for Smoother Selection
h.default(): Default Bandwidth
register.fd(): Curve Registration (Alignment)

Kernels (Smoothing)

Kernel(): Unified Symmetric Kernel Interface
Kernel.asymmetric(): Unified Asymmetric Kernel Interface
Kernel.integrate(): Unified Integrated Kernel Interface
Ker.cos(): Cosine Kernel
Ker.epa(): Epanechnikov Kernel
Ker.norm(): Kernel Functions
Ker.quar(): Quartic (Biweight) Kernel
Ker.tri(): Triweight Kernel
Ker.unif(): Uniform (Rectangular) Kernel
AKer.cos(): Asymmetric Cosine Kernel
AKer.epa(): Asymmetric Epanechnikov Kernel
AKer.norm(): Asymmetric Normal Kernel
AKer.quar(): Asymmetric Quartic Kernel
AKer.tri(): Asymmetric Triweight Kernel
AKer.unif(): Asymmetric Uniform Kernel
IKer.cos(): Integrated Cosine Kernel
IKer.epa(): Integrated Epanechnikov Kernel
IKer.norm(): Integrated Normal Kernel
IKer.quar(): Integrated Quartic Kernel
IKer.tri(): Integrated Triweight Kernel
IKer.unif(): Integrated Uniform Kernel

Covariance Functions (GP)

kernel.add(): Add Covariance Functions
kernel.brownian(): Brownian Motion Covariance Function
kernel.exponential(): Exponential Covariance Function
kernel.gaussian(): Gaussian (Squared Exponential) Covariance Function
kernel.linear(): Linear Covariance Function
kernel.matern(): Matern Covariance Function
kernel.mult(): Multiply Covariance Functions
kernel.periodic(): Periodic Covariance Function
kernel.polynomial(): Polynomial Covariance Function
kernel.whitenoise(): White Noise Covariance Function
make.gaussian.process(): Generate Gaussian Process Samples
cov(): Functional Covariance Function

Simulation

eFun(): Generate Eigenfunction Basis
eVal(): Generate Eigenvalue Sequence
simFunData(): Simulate Functional Data via Karhunen-Loeve Expansion
simMultiFunData(): Simulate Multivariate Functional Data
addError(): Add Measurement Error to Functional Data

Irregular Functional Data

irregFdata(): Create an Irregular Functional Data Object
is.irregular(): Check if an Object is Irregular Functional Data
sparsify(): Convert Regular Functional Data to Irregular by Subsampling
as.fdata(): Convert Irregular Functional Data to Regular Grid
mean(<irregFdata>): Estimate Mean Function for Irregular Data
summary(<irregFdata>): Summary method for irregFdata objects
print(<irregFdata>): Print method for irregFdata objects
autoplot(<irregFdata>): Autoplot method for irregFdata objects
plot(<irregFdata>): Plot method for irregFdata objects
`[`(<irregFdata>): Subset method for irregFdata objects

Random Processes

r.bridge(): Generate Brownian Bridge
r.brownian(): Generate Brownian Motion
r.ou(): Generate Ornstein-Uhlenbeck Process

Statistical Tests

fmean.test.fdata(): Test for Equality of Functional Means
fequiv.test(): Functional Equivalence Test (TOST)
group.test(): Permutation Test for Group Differences

Plotting

autoplot(<fdata>): Create a ggplot for fdata objects
plot(<fdata>): Plot method for fdata objects
boxplot(<fdata>): Functional Boxplot
plot(<fdata2pc>): Plot FPCA Results
plot(<basis.auto>): Plot method for basis.auto objects
plot(<basis.cv>): Plot method for basis.cv objects
plot(<cluster.fcm>): Plot Method for cluster.fcm Objects
plot(<cluster.gmm>): Plot Method for cluster.gmm Objects
plot(<cluster.kmeans>): Plot Method for cluster.kmeans Objects
plot(<cluster.optim>): Plot Method for cluster.optim Objects
plot(<fanova>): Plot method for fanova objects
plot(<fclassif>): Plot method for fclassif objects
plot(<fequiv.test>): Plot method for fequiv.test
plot(<fmm>): Plot method for fmm objects
plot(<fosr>): Plot method for fosr objects
plot(<group.distance>): Plot method for group.distance
plot(<outliergram>): Plot Method for Outliergram Objects
plot(<outliers.fdata>): Plot method for outliers.fdata objects
plot(<pspline>): Plot method for pspline objects
plot(<pspline.2d>): Plot method for pspline.2d objects
plot(<register.fd>): Plot Method for register.fd Objects
plot(<magnitudeshape>): Plot Method for magnitudeshape Objects
plot(<amplitude_modulation>): Plot method for amplitude_modulation objects
plot(<lomb_scargle_result>): Plot method for lomb_scargle_result objects
plot(<matrix_profile_result>): Plot method for matrix_profile_result objects
plot(<peak_detection>): Plot method for peak_detection objects
plot(<peak_timing>): Plot method for peak_timing objects
plot(<ssa_result>): Plot method for ssa_result objects
plot(<stl_result>): Plot method for stl_result objects

Prediction

predict(<cluster.gmm>): Predict Cluster Membership for New Functional Data
predict(<fosr>): Predict from Function-on-Scalar Regression
predict(<fregre.fd>): Predict Method for Functional Regression (fregre.fd)
predict(<fregre.lm>): Predict method for fregre.lm objects
predict(<fregre.np>): Predict Method for Nonparametric Functional Regression (fregre.np)
predict(<fregre.np.multi>): Predict method for fregre.np.multi

Print & Summary

print(<fdata>): Print method for fdata objects
print(<fdata2pc>): Print Method for FPCA Results
print(<fdata.bootstrap.ci>): Print method for bootstrap CI
print(<basis.auto>): Print method for basis.auto objects
print(<basis.cv>): Print method for basis.cv objects
print(<cluster.fcm>): Print Method for cluster.fcm Objects
print(<cluster.gmm>): Print Method for cluster.gmm Objects
print(<cluster.kmeans>): Print Method for cluster.kmeans Objects
print(<cluster.optim>): Print Method for cluster.optim Objects
print(<fanova>): Print method for fanova objects
print(<fbplot>): Print Method for fbplot Objects
print(<fclassif>): Print method for fclassif objects
print(<fclassif.cv>): Print method for fclassif.cv objects
print(<fmm>): Print method for fmm objects
print(<fmm.test>): Print method for fmm.test objects
print(<fosr>): Print method for fosr objects
print(<fregre.fd>): Print method for fregre objects
print(<fregre.lm>): Print method for fregre.lm objects
print(<fregre.logistic>): Print method for fregre.logistic objects
print(<fregre.np>): Print method for fregre.np objects
print(<fregre.np.multi>): Print method for fregre.np.multi
print(<group.distance>): Print method for group.distance
print(<fequiv.test>): Print method for fequiv.test
print(<group.test>): Print method for group.test
print(<kernel>): Print Method for Covariance Functions
print(<magnitudeshape>): Print Method for magnitudeshape Objects
print(<outliergram>): Print Method for Outliergram Objects
print(<outliers.fdata>): Print method for outliers.fdata objects
print(<pspline>): Print method for pspline objects
print(<pspline.2d>): Print method for pspline.2d objects
print(<register.fd>): Print Method for register.fd Objects
summary(<basis.auto>): Summary method for basis.auto objects
summary(<fdata>): Summary method for fdata objects
print(<amplitude_modulation>): Print method for amplitude_modulation objects
print(<autoperiod_result>): Print method for autoperiod_result objects
print(<cfd_autoperiod_result>): Print method for cfd_autoperiod_result objects
print(<decomposition>): Print method for decomposition objects
print(<lomb_scargle_result>): Print method for lomb_scargle_result objects
print(<matrix_profile_result>): Print method for matrix_profile_result objects
print(<multiFunData>): Print method for multiFunData objects
print(<multiple_periods>): Print method for multiple_periods objects
print(<peak_detection>): Print method for peak_detection objects
print(<peak_timing>): Print method for peak_timing objects
print(<period_estimate>): Print method for period_estimate objects
print(<sazed_result>): Print method for sazed_result objects
print(<seasonality_changes>): Print method for seasonality_changes objects
print(<seasonality_changes_auto>): Print method for seasonality_changes_auto objects
print(<seasonality_classification>): Print method for seasonality_classification objects
print(<ssa_result>): Print method for ssa_result objects
print(<stl_result>): Print method for stl_result objects

Other

`[`(<fdata>): Subset method for fdata objects
Ops(<fdata>): Arithmetic Operations for Functional Data
kernels: Covariance Kernel Functions for Gaussian Processes
fdars fdars-package: fdars: Functional Data Analysis in 'Rust'

Internal

Low-level Rust wrappers and internal functions

explain_ale_logistic_rust(): ALE for logistic model
explain_ale_rust(): Accumulated Local Effects
explain_anchor_logistic_rust(): Anchor for logistic model
explain_anchor_rust(): Anchor explanation
explain_beta_decomposition_logistic_rust(): Beta decomposition for logistic model
explain_beta_decomposition_rust(): Beta decomposition by FPC components
explain_calibration_rust(): Calibration diagnostics for logistic model
explain_conditional_importance_logistic_rust(): Conditional permutation importance for logistic model
explain_conditional_importance_rust(): Conditional permutation importance
explain_conformal_rust(): Conformal prediction intervals
explain_counterfactual_logistic_rust(): Counterfactual for logistic model (max_iter/step_size instead of target_value)
explain_counterfactual_rust(): Counterfactual explanation
explain_dfbetas_rust(): DFBETAS and DFFITS diagnostics
explain_domain_logistic_rust(): Domain selection for logistic model
explain_domain_rust(): Domain selection (important intervals)
explain_ece_rust(): Expected Calibration Error for logistic model
explain_friedman_logistic_rust(): Friedman H-statistic for logistic model
explain_friedman_rust(): Friedman H-statistic for interaction detection
explain_importance_logistic_rust(): Permutation importance for logistic model
explain_importance_rust(): FPC permutation importance
explain_influence_rust(): Influence diagnostics (leverage, Cook's distance)
explain_lime_logistic_rust(): LIME for logistic model
explain_lime_rust(): LIME local explanation
explain_loo_rust(): Leave-one-out cross-validation and PRESS
explain_pdp_logistic_rust(): PDP for logistic model
explain_pdp_rust(): Partial dependence plot for a single FPC component
explain_pointwise_importance_logistic_rust(): Pointwise importance for logistic model
explain_pointwise_importance_rust(): Pointwise importance of beta(t)
explain_prediction_intervals_rust(): Prediction intervals for new observations
explain_prototype_rust(): Prototype and criticism selection
explain_regression_depth_logistic_rust(): Regression depth for logistic model
explain_regression_depth_rust(): Regression depth
explain_saliency_logistic_rust(): Saliency map for logistic model (fit-only, no data)
explain_saliency_rust(): Functional saliency map
explain_shap_logistic_rust(): SHAP values for logistic model
explain_shap_rust(): SHAP values for FPC scores
explain_significant_regions_rust(): Significant regions from beta(t) and its standard errors
explain_sobol_logistic_rust(): Sobol indices for logistic model (no y, uses n_samples/seed)
explain_sobol_rust(): Sobol sensitivity indices
explain_stability_logistic_rust(): Stability for logistic model (no fit param)
explain_stability_rust(): Explanation stability via bootstrap
explain_vif_logistic_rust(): VIF for logistic model
explain_vif_rust(): Variance inflation factors for FPC scores
elastic_amp_changepoint_rust(): Amplitude changepoint detection
elastic_cut_dendrogram_rust(): Cut a hierarchical dendrogram to get cluster labels
elastic_fpca_changepoint_rust(): FPCA-based changepoint detection
elastic_hierarchical_rust(): Elastic hierarchical clustering
elastic_horiz_fpca_rust(): Horizontal FPCA on warping functions
elastic_joint_fpca_rust(): Joint FPCA (amplitude + phase)
elastic_kmeans_rust(): Elastic k-means clustering
elastic_logistic_rust(): Elastic logistic regression
elastic_pcr_attribution_rust(): Elastic PCR attribution (amplitude vs phase importance)
elastic_pcr_rust(): Elastic principal component regression
elastic_ph_changepoint_rust(): Phase changepoint detection
elastic_regression_rust(): Elastic regression (function-on-scalar with alignment)
elastic_spm_monitor_rust(): Elastic SPM Phase II monitoring.
elastic_spm_phase1_rust(): Elastic SPM Phase I.
elastic_vert_fpca_rust(): Vertical FPCA on elastic-aligned data
smooth_basis_gcv_rust(): Smooth functional data with GCV-selected penalty
smooth_basis_rust(): Smooth functional data using B-spline or Fourier basis with fixed penalty
alignment_diagnose_pairwise_rust(): Diagnose a single pairwise alignment
alignment_diagnose_rust(): Diagnose alignment quality for all curves after Karcher mean computation
alignment_elastic_pair_penalized_rust(): Penalized elastic alignment with configurable penalty type
alignment_invert_warp_rust(): Invert a warping function
alignment_lambda_cv_rust(): Lambda cross-validation for elastic alignment regularisation
alignment_orbit_representative_rust(): Compute the orbit representative of a curve in a quotient space
alignment_phase_boxplot_rust(): Phase boxplot for warping functions
alignment_shape_distance_rust(): Elastic shape distance between two curves
alignment_shape_mean_rust(): Shape mean (Karcher mean in quotient space)
alignment_shape_self_distance_matrix_rust(): Shape self-distance matrix
alignment_warp_inverse_error_rust(): Warp inverse error: max |gamma(gamma_inv(t)) - t|
alignment_warp_statistics_rust(): Warp statistics: mean, variance, confidence bands, Karcher mean warp
basis2fdata_2d_rust(): Reconstruct 2D functional data from tensor product basis coefficients (low-level)
bootstrap_ci_fregre_lm_rust(): Bootstrap confidence intervals for β(t) in functional linear model
bootstrap_ci_functional_logistic_rust(): Bootstrap confidence intervals for β(t) in functional logistic model
conformal_classif_rust(): Conformal classification
conformal_elastic_logistic_rust(): Conformal prediction for elastic logistic regression
conformal_elastic_pcr_rust(): Conformal prediction for elastic PCR
conformal_elastic_regression_rust(): Conformal prediction for elastic regression
conformal_fregre_lm_rust(): Conformal prediction for functional linear model
conformal_fregre_np_rust(): Conformal prediction for nonparametric functional regression
conformal_generic_classification_logistic_rust(): Generic conformal classification for a pre-fitted logistic model
conformal_generic_regression_lm_rust(): Generic conformal regression for a pre-fitted fregre.lm model
conformal_logistic_rust(): Conformal prediction for logistic regression
cv_conformal_classif_rust(): CV-conformal classification
cv_conformal_fregre_lm_rust(): CV-conformal regression using functional linear model
cv_conformal_fregre_np_rust(): CV-conformal regression using nonparametric kernel regression
depth_rpd_1d_rust(): Random Projection Depth with Derivatives (seeded)
fanova_rust(): Functional ANOVA
fclassif_cv_rust(): Cross-validated classification
fclassif_dd_rust(): DD-plot classification
fclassif_kernel_rust(): Kernel classification
fclassif_knn_rust(): kNN classification
fclassif_lda_rust(): LDA classification
fclassif_qda_rust(): QDA classification
fdata2basis_2d_rust(): Project 2D functional data to tensor product basis coefficients (low-level)
fdata_gradient_rust(): High-accuracy gradient using 5-point stencil (uniform) or 3-point Lagrange (non-uniform)
fmm_predict_rust(): Predict from functional mixed model
fmm_rust(): Functional mixed model
fmm_test_fixed_rust(): Permutation test for fixed effects in FMM
fosr_2d_rust(): 2D Function-on-scalar regression with tensor-product penalties
fosr_fpc_rust(): FPC-based function-on-scalar regression
fosr_rust(): Function-on-scalar regression (penalized)
frcc_monitor_rust(): FRCC Phase II: Monitor new data against a functional regression control chart.
frcc_phase1_rust(): FRCC Phase I: Build a functional regression control chart.
fregre_cv_rust(): Cross-validation for FPC component selection
fregre_huber_rust(): Huber M-estimation functional regression
fregre_l1_rust(): L1 (LAD) functional regression
fregre_lm_rust(): Functional linear model (FPC-based)
fregre_np_mixed_rust(): Nonparametric functional regression with mixed predictors
functional_logistic_rust(): Functional logistic regression
gmm_cluster_rust(): GMM clustering with automatic K selection
gmm_em_rust(): Raw GMM EM on feature matrix
jackknife_plus_fregre_lm_rust(): Jackknife+ regression using functional linear model
jackknife_plus_fregre_np_rust(): Jackknife+ regression using nonparametric kernel regression
mfpca_rust(): Multivariate FPCA.
model_selection_ncomp_rust(): Model selection for ncomp via AIC/BIC/GCV
outliers_thres_lrt_with_dist_rust(): LRT outlier threshold with full bootstrap distribution
predict_elastic_logistic_rust(): Predict from elastic logistic regression
predict_elastic_regression_rust(): Predict from elastic regression
predict_fosr_2d_rust(): Predict from 2D function-on-scalar regression
predict_fosr_rust(): Predict from function-on-scalar regression
predict_fregre_robust_rust(): Predict from robust (L1/Huber) regression
predict_functional_logistic_rust(): Predict from functional logistic regression
predict_gmm_rust(): Predict from GMM
predict_scalar_on_shape_rust(): Predict from a scalar-on-shape model
scalar_on_shape_rust(): Fit scalar-on-shape regression model
spm_amewma_monitor_rust(): Adaptive EWMA (AMEWMA) monitoring on sequential functional data.
spm_arl0_ewma_t2_rust(): In-control ARL for EWMA-T-squared chart.
spm_arl0_spe_rust(): In-control ARL for SPE chart.
spm_arl0_t2_rust(): In-control ARL for T-squared chart.
spm_arl1_t2_rust(): Out-of-control ARL for T-squared chart.
spm_cusum_monitor_restart_rust(): CUSUM monitoring with restart after each alarm.
spm_cusum_monitor_rust(): CUSUM monitoring on sequential functional data.
spm_evaluate_rules_rust(): Evaluate control chart rules (Western Electric, Nelson, or custom).
spm_ewma_rust(): EWMA-based SPM monitoring.
spm_mewma_monitor_rust(): MEWMA monitoring on sequential functional data.
spm_monitor_partial_batch_rust(): Monitor a batch of partially-observed curves.
spm_monitor_partial_rust(): Monitor a single partially-observed curve.
spm_monitor_rust(): SPM Phase II: Monitor new data against an established chart.
spm_phase1_iterative_rust(): Iterative Phase I chart construction.
spm_phase1_rust(): SPM Phase I: Build a univariate control chart from in-control functional data.
spm_profile_monitor_rust(): Profile monitoring Phase II.
spm_profile_phase1_rust(): Profile monitoring Phase I.
spm_select_ncomp_rust(): Select the number of principal components.
spm_spe_contrib_rust(): SPE contribution diagnostics.
spm_spe_limit_robust_rust(): Robust SPE control limit.
spm_t2_contrib_rust(): T-squared contribution diagnostics.
spm_t2_limit_robust_rust(): Robust T-squared control limit.
spm_t2_pc_contrib_rust(): Per-PC T-squared contributions for a single observation or batch.
tolerance_elastic_config_rust(): Elastic tolerance band with full config (amplitude + phase)
tolerance_phase_rust(): Phase tolerance band (warping variation)
add_error_curve_1d(): Add curve-level Gaussian noise to functional data
add_error_pointwise_1d(): Add pointwise Gaussian noise to functional data
alignment_align_to_target(): Align all curves to a target curve
alignment_amplitude_dist(): Amplitude self-distance matrix
alignment_bayesian_pair(): Bayesian pairwise alignment via pCN MCMC on the Hilbert sphere
alignment_closed_distance(): Elastic distance between two closed curves (optimizes over rotations)
alignment_closed_pair(): Elastic alignment for closed (periodic) curves with rotation search
alignment_compose_warps(): Compose two warping functions
alignment_constrained(): Elastic alignment with explicit landmark constraints
alignment_cross_dist(): Elastic cross-distance matrix
alignment_curve_geodesic(): Geodesic path between two curves in the elastic metric
alignment_decomposition(): Elastic phase-amplitude decomposition
alignment_elastic_depth(): Elastic depth (amplitude + phase + combined)
alignment_elastic_distance(): Elastic (Fisher-Rao) distance between two curves
alignment_elastic_outlier(): Elastic outlier detection using Tukey fence on elastic distances
alignment_elastic_pair(): Elastic alignment of one curve to another
alignment_gauss_model(): Gaussian generative model: sample random curves from aligned data
alignment_horiz_fpns(): Horizontal Functional Principal Nested Spheres (FPNS) for phase variability
alignment_joint_gauss_model(): Joint Gaussian generative model preserving amplitude-phase correlation
alignment_karcher_mean(): Karcher (Fréchet) mean in elastic metric
alignment_karcher_mean_closed(): Karcher mean for closed (periodic) curves
alignment_karcher_median(): Karcher median (Weiszfeld algorithm in elastic metric)
alignment_multires_pair(): Multi-resolution elastic alignment (coarse DP + fine gradient refinement)
alignment_pairwise_consistency(): Pairwise alignment consistency
alignment_partial_match(): Elastic partial matching of a template within a longer target curve
alignment_peak_persistence(): Peak persistence diagram across a sweep of lambda values
alignment_phase_dist(): Phase self-distance matrix
alignment_quality_compute(): Compute alignment quality metrics
alignment_reparameterize(): Apply warping function to reparameterize a curve
alignment_robust_karcher_mean(): Robust (trimmed) Karcher mean in elastic metric
alignment_self_dist(): Elastic self-distance matrix
alignment_shape_ci(): Bootstrap shape confidence intervals for the elastic Karcher mean
alignment_srsf_inverse(): Inverse SRSF: reconstruct curve from SRSF representation
alignment_srsf_transform(): SRSF transform of functional data
alignment_transfer(): Transfer alignment: align curves across populations
alignment_tsrvf_from_karcher(): Compute TSRVF from a pre-computed Karcher mean
alignment_tsrvf_inverse(): Inverse TSRVF: reconstruct curves from tangent vectors
alignment_tsrvf_transform(): Full TSRVF transform: compute Karcher mean + transport to tangent space
alignment_warp_complexity(): Compute warp complexity (geodesic distance from identity)
alignment_warp_smoothness(): Compute warp smoothness (bending energy)
alignment_with_landmarks(): Elastic alignment with automatic landmark detection
andrews_loadings(): Andrews Loadings: Project FPCA Eigenfunctions to Original Variables
andrews_transform(): Andrews Transformation
basis_aic_1d(): Compute AIC for basis fit AIC = n * log(RSS/n) + 2 * total_edf Where total_edf = n_curves * edf (each curve has edf parameters) When pooled=true: compute single AIC across all curves When pooled=false: compute per-curve AIC and return mean
basis_bic_1d(): Compute BIC for basis fit BIC = n * log(RSS/n) + log(n) * total_edf Where total_edf = n_curves * edf (each curve has edf parameters) When pooled=true: compute single BIC across all curves When pooled=false: compute per-curve BIC and return mean
basis_gcv_1d(): Compute GCV score for basis fit GCV = RSS/n / (1 - edf/n)^2 When pooled=true: compute single GCV across all curves When pooled=false: compute per-curve GCV and return mean
calinski_harabasz(): Compute Calinski-Harabasz index (variance ratio criterion) Higher values indicate better defined clusters
compute_adot(): Compute the Adot matrix (parallelized)
depth_bd_1d(): Band Depth (BD) for 1D functional data BD(x) = proportion of pairs (i,j) where x lies within the band formed by curves i and j A curve lies in the band if at every time point t, min(X_i(t), X_j(t)) <= x(t) <= max(X_i(t), X_j(t))
depth_fm_1d(): Compute Fraiman-Muniz depth
depth_fm_2d(): Fraiman-Muniz depth for 2D functional data (surfaces) Integrates univariate depth over (s,t) grid
depth_fsd_1d(): Compute Functional Spatial Depth
depth_fsd_2d(): Functional Spatial Depth for 2D functional data
depth_kfsd_1d(): Kernel Functional Spatial Depth (KFSD) for 1D functional data Implements the RKHS-based formulation matching fda.usc h is treated as the actual bandwidth, matching how fda.usc uses hq2 argvals is used for trapezoidal integration to compute L2 norms
depth_kfsd_2d(): Kernel Functional Spatial Depth (KFSD) for 2D functional data Implements the RKHS-based formulation matching fda.usc
depth_mbd_1d(): Modified Band Depth (MBD) for 1D functional data MBD(x) = average over pairs (i,j) of the proportion of the domain where x is inside the band This is more robust than BD as it doesn't require complete containment
depth_mei_1d(): Modified Epigraph Index (MEI) for 1D functional data MEI measures the proportion of time a curve is below other curves MEI(x_i) = (1/n) * sum_j (1/m) * sum_t I(x_i(t) < x_j(t)) + 0.5*I(x_i(t) = x_j(t))
depth_mode_1d(): Compute modal depth
depth_mode_2d(): Modal depth for 2D functional data (surfaces) Uses L2 distance in the flattened surface space
depth_rp_1d(): Compute random projection depth
depth_rp_1d_seeded(): Random projection depth with optional seed
depth_rp_2d(): Random projection depth for 2D functional data (surfaces) Projects surfaces to scalars using random projections
depth_rt_1d(): Compute random Tukey depth
depth_rt_1d_seeded(): Random Tukey depth with optional seed
depth_rt_2d(): Random Tukey depth for 2D functional data (surfaces)
detect_amplitude_modulation(): Detect Amplitude Modulation in Seasonal Time Series
df_to_fdata2d(): Convert DataFrame to 2D functional data
fdata2basis_cv(): Cross-Validation for Basis Function Number Selection
fdata_center_1d(): Center functional data by subtracting the mean function
fdata_deriv_1d(): Compute numerical derivative of functional data (parallelized over rows)
fdata_deriv_2d(): Compute 2D partial derivatives for surface data
fdata_mean_1d(): Compute the mean function across all samples (1D)
fdata_mean_2d(): Compute the mean function across all samples (2D surfaces) Data is stored as n x (m1*m2) matrix where each row is a flattened surface
fdata_norm_lp_1d(): Compute Lp norm for each sample
fuzzycmeans_fd(): Fuzzy C-Means clustering for functional data m_fuzz is the fuzziness parameter (typically 2)
geometric_median_1d(): Compute the geometric median (L1 median) of functional data using Weiszfeld's algorithm The geometric median minimizes sum of L2 distances to all curves
geometric_median_2d(): Compute the geometric median (L1 median) of 2D functional data using Weiszfeld's algorithm Data is stored as n x (m1*m2) matrix where each row is a flattened surface
inprod_fdata(): Inner product of two functional data objects <f, g> = integral(f(t) * g(t) dt)
int_simpson(): Simpson's rule integration for functional data Integrates each curve over the domain
irreg_fdata2basis(): Fit basis functions to irregular functional data Each curve is individually fitted via least squares at its own observation points basis_type: 0 = bspline, 1 = fourier
irreg_integrate(): Compute integral for each curve in irregular functional data
irreg_mean_kernel(): Estimate mean function for irregular data using kernel smoothing
irreg_metric_lp(): Compute pairwise Lp distances for irregular functional data
irreg_norm_lp(): Compute Lp norm for each curve in irregular functional data
irreg_to_regular(): Convert irregular data to regular grid via interpolation
kmeans_fd(): Functional k-means clustering
knn_gcv(): k-NN with Global Cross-Validation Finds a single optimal k for all observations
knn_lcv(): k-NN with Local Cross-Validation Finds an optimal k for each observation
knn_predict(): Kernel prediction with fixed bandwidth for prediction on new data
landmark_detect(): Detect landmarks in a single curve
landmark_register_curves(): Detect landmarks and register curves
metric_dtw_cross_1d(): Compute DTW distance matrix for cross-distances (n1 x n2)
metric_dtw_self_1d(): Compute DTW distance matrix for self-distances (symmetric)
metric_hausdorff_1d(): Compute Hausdorff distance matrix for self-distances (symmetric)
metric_hausdorff_2d(): Compute Hausdorff distance for 2D functional data (surfaces)
metric_hausdorff_cross_1d(): Compute Hausdorff distance matrix for cross-distances (n1 x n2)
metric_hausdorff_cross_2d(): Compute Hausdorff cross-distances for 2D functional data
metric_kl_cross_1d(): Compute symmetric KL divergence matrix for cross-distances (1D)
metric_kl_self_1d(): Compute symmetric KL divergence matrix for self-distances (1D) Curves are first normalized to be valid probability distributions
metric_lp_1d(): Compute Lp distance matrix between two sets of functional data
metric_lp_2d(): Compute Lp distance between two 2D functional data objects (surfaces)
metric_lp_self_1d(): Compute Lp distance matrix for self-distances (symmetric)
metric_lp_self_2d(): Compute Lp self-distance matrix for 2D functional data (symmetric)
metric_soft_dtw_barycenter(): Soft-DTW barycenter computation
metric_soft_dtw_cross_1d(): Soft-DTW cross-distance matrix
metric_soft_dtw_div_cross_1d(): Soft-DTW divergence cross-distance matrix
metric_soft_dtw_div_self_1d(): Soft-DTW divergence self-distance matrix
metric_soft_dtw_self_1d(): Soft-DTW self-distance matrix
outlier_summary(): Unified Outlier Summary
outliers_lrt(): LRT-based outlier detection Returns indices of detected outliers
outliers_thres_lrt(): Compute bootstrap threshold for LRT outlier detection Highly parallelized across bootstrap iterations
pcvm_statistic(): Compute the PCvM statistic
plot(<amplitude_modulation>): Plot method for amplitude_modulation objects
plot(<lomb_scargle_result>): Plot method for lomb_scargle_result objects
plot(<matrix_profile_result>): Plot method for matrix_profile_result objects
plot(<peak_detection>): Plot method for peak_detection objects
plot(<peak_timing>): Plot method for peak_timing objects
plot(<ssa_result>): Plot method for ssa_result objects
plot(<stl_result>): Plot method for stl_result objects
print(<amplitude_modulation>): Print method for amplitude_modulation objects
print(<autoperiod_result>): Print method for autoperiod_result objects
print(<cfd_autoperiod_result>): Print method for cfd_autoperiod_result objects
print(<lomb_scargle_result>): Print method for lomb_scargle_result objects
print(<matrix_profile_result>): Print method for matrix_profile_result objects
print(<multiple_periods>): Print method for multiple_periods objects
print(<peak_detection>): Print method for peak_detection objects
print(<peak_timing>): Print method for peak_timing objects
print(<period_estimate>): Print method for period_estimate objects
print(<sazed_result>): Print method for sazed_result objects
print(<seasonality_changes>): Print method for seasonality_changes objects
print(<seasonality_changes_auto>): Print method for seasonality_changes_auto objects
print(<seasonality_classification>): Print method for seasonality_classification objects
print(<ssa_result>): Print method for ssa_result objects
print(<stl_result>): Print method for stl_result objects
pspline_fit_1d(): P-spline fitting: returns coefficients, fitted values, and diagnostics
pspline_fit_2d(): 2D P-spline fitting with anisotropic penalties
register_shift_1d(): Shift registration: find optimal horizontal shift for each curve to align with a target (usually the mean)
rp_stat(): Compute random projection statistics (parallelized over projections)
s_knn(): K-Nearest Neighbors smoother matrix
s_llr(): Local Linear Regression smoother matrix Uses weighted least squares with degree-1 polynomial
s_lpr(): Local Polynomial Regression smoother matrix Solves (p+1)×(p+1) weighted least squares system for each point
s_nw(): Nadaraya-Watson smoother matrix S_ij = K((t_i - t_j)/h) * w_j / sum_k(K((t_i - t_k)/h) * w_k)
scale_minmax(): Min-Max scaling for functional data
seasonal_analyze_peak_timing(): Analyze peak timing variability across cycles (uses Fourier smoothing)
seasonal_autoperiod(): Autoperiod: Hybrid FFT + ACF period detection with gradient ascent refinement Returns period, confidence, FFT power, ACF validation score, and candidates
seasonal_cfd_autoperiod(): CFDAutoperiod: Clustered Filtered Detrended Autoperiod Uses differencing for detrending and clustering for robust period detection
seasonal_classify_seasonality(): Classify seasonality type
seasonal_decompose(): Decompose functional data into trend, seasonal, and remainder components
seasonal_detect_amplitude_modulation(): Detect amplitude modulation in seasonal time series using Hilbert transform
seasonal_detect_amplitude_modulation_wavelet(): Detect amplitude modulation using wavelet transform (Morlet wavelet)
seasonal_detect_changes(): Detect seasonality changes (onset/cessation)
seasonal_detect_changes_auto(): Detect seasonality changes with automatic threshold
seasonal_detect_multiple_periods(): Detect multiple concurrent periodicities using iterative residual subtraction
seasonal_detect_peaks(): Detect peaks in functional data using Fourier basis smoothing
seasonal_detrend(): Detrend functional data using specified method Returns trend, detrended data, method used, RSS per curve, and number of parameters
seasonal_estimate_period_acf(): Estimate period using autocorrelation
seasonal_estimate_period_fft(): Estimate period using FFT periodogram
seasonal_instantaneous_period(): Estimate instantaneous period using Hilbert transform
seasonal_lomb_scargle(): Lomb-Scargle periodogram for irregularly sampled data Computes the power spectrum and significance for period detection
seasonal_matrix_profile(): Matrix Profile for motif discovery and period detection Uses STOMP algorithm for efficient computation
seasonal_sazed(): SAZED: Spectral-ACF Zero-crossing Ensemble Detection A parameter-free ensemble method for robust period detection Returns period, confidence, component periods, and agreeing component count
seasonal_ssa(): Singular Spectrum Analysis for time series decomposition Extracts trend, seasonal, and noise components via SVD
seasonal_stl(): STL (Seasonal and Trend decomposition using LOESS) Implements Cleveland et al. 1990 algorithm
seasonal_strength_spectral(): Measure seasonal strength using spectral method
seasonal_strength_variance(): Measure seasonal strength using variance decomposition
seasonal_strength_wavelet(): Measure seasonal strength using wavelet (Morlet) method
seasonal_strength_windowed(): Time-varying seasonal strength using sliding windows
select_basis_auto(): Automatic basis selection for each curve individually.
semimetric_basis_cross_1d(): Basis coefficient semimetric (cross-distances)
semimetric_basis_self_1d(): Basis coefficient semimetric (self-distances)
semimetric_deriv_cross_1d(): Derivative-based semimetric (cross-distances)
semimetric_deriv_self_1d(): Derivative-based semimetric (self-distances)
semimetric_fourier_cross_1d(): Compute semimetric based on Fourier coefficients for cross-distances
semimetric_fourier_self_1d(): Compute semimetric based on Fourier coefficients for self-distances (symmetric) Uses FFT to compute Fourier coefficients and then L2 distance on coefficients
semimetric_hshift_cross_1d(): Compute semimetric based on horizontal shift for cross-distances
semimetric_hshift_self_1d(): Compute semimetric based on horizontal shift for self-distances (symmetric) This finds the minimum L2 distance after optimally shifting one curve horizontally
semimetric_pca_cross_1d(): PCA-based semimetric (cross-distances)
semimetric_pca_self_1d(): PCA-based semimetric (self-distances)
silhouette_score(): Compute silhouette score for clustering Returns the mean silhouette coefficient across all samples
sim_kl_1d(): Simulate functional data via Karhunen-Loève expansion
streaming_depth_batch(): Streaming depth: batch self-depth computation
streaming_depth_one(): Streaming depth: single curve against reference
streaming_depth_vs_ref(): Streaming depth: new data against reference
tolerance_conformal(): Conformal prediction band
tolerance_elastic(): Elastic tolerance band (alignment + FPCA)
tolerance_exponential(): Exponential family tolerance band
tolerance_fpca(): FPCA-based tolerance band
tolerance_scb_degras(): SCB mean confidence band (Degras method)
basis2fdata_1d(): Reconstruct functional data from basis coefficients Returns data matrix (n x m)
eigenfunctions_1d(): Compute eigenfunction basis values efun_type: 0 = Fourier, 1 = Poly, 2 = PolyHigh, 3 = Wiener
eigenvalues_1d(): Generate eigenvalue sequence eval_type: 0 = linear, 1 = exponential, 2 = wiener
fdata2basis_1d(): Convert functional data to basis coefficients type: 0 = bspline, 1 = fourier
fdata2pc_1d(): Perform functional PCA via SVD on centered data Returns: singular values, rotation matrix (loadings), scores, mean
fdata2pls_1d(): Perform PLS via NIPALS algorithm Returns: weights, scores, loadings