# Functional Data Analysis in 'Rust'

## Documentation for package ‘fdars’ version 0.3.3

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E F G H I K L M N O P R S T V misc

## -- A --

addError | Add Measurement Error to Functional Data
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
analyze.peak.timing | Analyze Peak Timing Variability
as.fdata | Convert Irregular Functional Data to Regular Grid
as.fdata.fdata | Convert Irregular Functional Data to Regular Grid
as.fdata.irregFdata | Convert Irregular Functional Data to Regular Grid
autoperiod | Autoperiod: Hybrid FFT + ACF Period Detection
autoplot.fdata | Create a ggplot for fdata objects
autoplot.irregFdata | Autoplot method for irregFdata objects

## -- B --

basis.aic | AIC for Basis Representation
basis.bic | BIC for Basis Representation
basis.gcv | GCV Score for Basis Representation
basis2fdata | Basis Representation Functions for Functional Data
basis2fdata_2d | Reconstruct 2D Functional Data from Tensor Product Basis Coefficients
boxplot.fdata | Functional Boxplot

## -- C --

cfd.autoperiod | CFDAutoperiod: Clustered Filtered Detrended Autoperiod
classify.seasonality | Classify Seasonality Type
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
cov | Functional Covariance Function
CV.S | Cross-Validation for Smoother Selection

## -- D --

decompose | Seasonal-Trend Decomposition
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
deriv | Compute functional derivative
detect.peaks | Detect Peaks in Functional Data
detect.period | Seasonal Analysis Functions for Functional Data
detect.periods | Detect Multiple Concurrent Periods
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
detrend | Remove Trend from Functional Data
df_to_fdata2d | Convert DataFrame to 2D functional data

## -- E --

eFun | Generate Eigenfunction Basis
estimate.period | Estimate Seasonal Period using FFT
eVal | Generate Eigenvalue Sequence

## -- F --

fdata | Create a functional data object
fdata.bootstrap | Bootstrap Functional Data
fdata.bootstrap.ci | Bootstrap Confidence Intervals for Functional Statistics
fdata.cen | Center functional data
fdata2basis | Convert Functional Data to Basis Coefficients
fdata2basis.fdata | Convert Functional Data to Basis Coefficients
fdata2basis.irregFdata | 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
fdata2fd | Convert Functional Data to fd class
fdata2pc | Convert Functional Data to Principal Component Scores
fdata2pls | Convert Functional Data to PLS Scores
fequiv.test | Functional Equivalence Test (TOST)
flm.test | Statistical Tests for Functional Data
fmean.test.fdata | Test for Equality of Functional Means
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

## -- G --

GCV.S | Generalized Cross-Validation for Smoother Selection
gmed | Geometric Median of Functional Data
group.distance | Compute Distance/Similarity Between Groups of Functional Data
group.test | Permutation Test for Group Differences

## -- H --

h.default | Default Bandwidth

## -- I --

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
inprod.fdata | Inner Product of Functional Data
instantaneous.period | Estimate Instantaneous Period
int.simpson | Utility Functions for Functional Data Analysis
int.simpson.fdata | Utility Functions for Functional Data Analysis
int.simpson.irregFdata | Utility Functions for Functional Data Analysis
irregFdata | Create an Irregular Functional Data Object
is.irregular | Check if an Object is Irregular Functional Data

## -- K --

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
Kernel | Unified Symmetric Kernel Interface
kernel.add | Add Covariance Functions
Kernel.asymmetric | Unified Asymmetric Kernel Interface
kernel.brownian | Brownian Motion Covariance Function
kernel.exponential | Exponential Covariance Function
kernel.gaussian | Gaussian (Squared Exponential) Covariance Function
Kernel.integrate | Unified Integrated Kernel Interface
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
kernels | Covariance Kernel Functions for Gaussian Processes

## -- L --

localavg.fdata | Local Averages Feature Extraction
lomb.scargle | Lomb-Scargle Periodogram

## -- M --

magnitudeshape | Magnitude-Shape Outlier Detection for Functional Data
make.gaussian.process | Generate Gaussian Process Samples
matrix.profile | Matrix Profile for Motif Discovery and Period Detection
mean.fdata | Compute functional mean
mean.irregFdata | Estimate Mean Function for Irregular Data
median | Compute Functional Median
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.lp.fdata | Lp Metric for Functional Data
metric.lp.irregFdata | Lp Metric for Functional Data

## -- N --

norm | Compute Lp Norm of Functional Data
norm.fdata | Compute Lp Norm of Functional Data
norm.irregFdata | Compute Lp Norm of Functional Data
normalize | Normalize functional data

## -- O --

Ops.fdata | Arithmetic Operations for Functional Data
optim.np | Optimize Bandwidth Using Cross-Validation
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

## -- P --

plot.amplitude_modulation | Plot method for amplitude_modulation objects
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.fdata | Plot method for fdata objects
plot.fdata2pc | Plot FPCA Results
plot.fequiv.test | Plot method for fequiv.test
plot.group.distance | Plot method for group.distance
plot.irregFdata | Plot method for irregFdata objects
plot.lomb_scargle_result | Plot method for lomb_scargle_result objects
plot.magnitudeshape | Plot Method for magnitudeshape Objects
plot.matrix_profile_result | Plot method for matrix_profile_result objects
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.ssa_result | Plot method for ssa_result objects
plot.stl_result | Plot method for stl_result objects
pred.MAE | Mean Absolute Error
pred.MSE | Mean Squared Error
pred.R2 | R-Squared (Coefficient of Determination)
pred.RMSE | Root Mean Squared Error
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.amplitude_modulation | Print method for amplitude_modulation objects
print.autoperiod_result | Print method for autoperiod_result objects
print.basis.auto | Print method for basis.auto objects
print.basis.cv | Print method for basis.cv objects
print.cfd_autoperiod_result | Print method for cfd_autoperiod_result 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.decomposition | Print method for decomposition objects
print.fbplot | Print Method for fbplot Objects
print.fdata | Print method for fdata objects
print.fdata.bootstrap.ci | Print method for bootstrap CI
print.fdata2pc | Print Method for FPCA Results
print.fequiv.test | Print method for fequiv.test
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.irregFdata | Print method for irregFdata objects
print.kernel | Print Method for Covariance Functions
print.lomb_scargle_result | Print method for lomb_scargle_result objects
print.magnitudeshape | Print Method for magnitudeshape 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.outliergram | Print Method for Outliergram Objects
print.outliers.fdata | Print method for outliers.fdata 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.pspline | Print method for pspline objects
print.pspline.2d | Print method for pspline.2d objects
print.register.fd | Print Method for register.fd 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 | P-spline Smoothing for Functional Data
pspline.2d | P-spline Smoothing for 2D Functional Data

## -- R --

r.bridge | Generate Brownian Bridge
r.brownian | Generate Brownian Motion
r.ou | Generate Ornstein-Uhlenbeck Process
register.fd | Curve Registration (Alignment)

## -- S --

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
sazed | SAZED: Spectral-ACF Zero-crossing Ensemble Detection
scale_minmax | Min-Max scaling for functional data
scale_minmax.fdata | Min-Max scaling for functional data
scale_minmax.irregFdata | Min-Max scaling for functional data
sd | Functional Standard Deviation
seasonal.strength | Measure Seasonal Strength
seasonal.strength.curve | Time-Varying Seasonal Strength
select.basis.auto | Automatic Per-Curve Basis Type and Number Selection
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
simFunData | Simulate Functional Data via Karhunen-Loeve Expansion
simMultiFunData | Simulate Multivariate Functional Data
sparsify | Convert Regular Functional Data to Irregular by Subsampling
ssa.fd | Singular Spectrum Analysis (SSA) for Time Series Decomposition
standardize | Standardize functional data (z-score normalization)
standardize.fdata | Standardize functional data (z-score normalization)
standardize.irregFdata | Standardize functional data (z-score normalization)
stl.fd | STL Decomposition: Seasonal and Trend decomposition using LOESS
summary.basis.auto | Summary method for basis.auto objects
summary.fdata | Summary method for fdata objects
summary.irregFdata | Summary method for irregFdata objects

## -- T --

trimmed | Compute Functional Trimmed Mean
trimvar | Compute Functional Trimmed Variance

## -- V --

var | Functional Variance

## -- misc --

[.fdata | Subset method for fdata objects
[.irregFdata | Subset method for irregFdata objects