Package index • fdars

Functional Data Objects

fdata(): Create a functional data object
fdata.cen(): Center functional data
deriv(): Compute functional derivative
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

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
depth.RPD(): Random Projection Depth with Derivatives
depth.RT(): Random Tukey Depth

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
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.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
magnitudeshape(): Magnitude-Shape Outlier Detection for Functional Data

Regression

fregre.basis(): Functional Basis Regression
fregre.basis.cv(): Cross-Validation for Functional Basis Regression
fregre.np(): Nonparametric Functional Regression
fregre.np.cv(): Cross-Validation for Nonparametric Functional Regression
fregre.np.multi(): Nonparametric Regression with Multiple Functional Predictors
fregre.pc(): Functional Regression
fregre.pc.cv(): Cross-Validation for Functional PC Regression
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

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

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

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
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.kmeans>): Plot Method for cluster.kmeans Objects
plot(<cluster.optim>): Plot Method for cluster.optim 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

Prediction

predict(<fregre.fd>): Predict Method for Functional Regression (fregre.fd)
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.kmeans>): Print Method for cluster.kmeans Objects
print(<cluster.optim>): Print Method for cluster.optim Objects
print(<fbplot>): Print Method for fbplot Objects
print(<fregre.fd>): Print method for fregre 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(<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

Other

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