Title:	Supervised Classification for Functional Data via Signed Depth
Version:	0.1.0
Description:	Provides a suite of supervised classifiers for functional data based on the concept of signed depth. The core pipeline computes Fraiman-Muniz (FM) functional depth in either its Tukey or Simplicial variant, derives a signed depth by comparing each curve to a reference median curve via the signed distance integral, and feeds the resulting scalar summary into several classifiers: the k-Ranked Nearest Neighbour (k-RNN) rule, a moving-average smoother, a kernel-density Bayes rule, logistic regression on signed depth and distance to the mode, and a generalised additive model (GAM) classifier. Cross-validation routines for tuning the neighbourhood size k and parametric bootstrap confidence intervals are also included.
License:	GPL-3
Encoding:	UTF-8
Language:	en-GB
RoxygenNote:	7.3.1
Depends:	R (≥ 4.1.0)
Imports:	stats, graphics, mgcv, modeest
Suggests:	testthat (≥ 3.0.0), spelling, knitr, rmarkdown
Config/testthat/edition:	3
URL:	https://github.com/dapr12/fdclassify
BugReports:	https://github.com/dapr12/fdclassify/issues
NeedsCompilation:	no
Packaged:	2026-04-22 14:53:05 UTC; mbbxkdp3
Author:	Diego Andrés Pérez Ruiz [aut, cre], Peter Foster [ths]
Maintainer:	Diego Andrés Pérez Ruiz <diego.perezruiz@manchester.ac.uk>
Repository:	CRAN
Date/Publication:	2026-04-23 20:10:03 UTC

Bayesian Kernel-Density Classifier on Signed Depth

Description

Classifies functional curves via Bayes' rule applied to the signed depths. Class-conditional densities f_g(\mathrm{sdp}) are estimated by kernel density estimation; prior probabilities are estimated from class frequencies or supplied by the user.

Usage

bayes_depth_classify(
  X_train,
  y_train,
  X_test = NULL,
  priors = NULL,
  bw_method = "nrd0",
  grid = NULL,
  type = c("tukey", "simplicial")
)

## S3 method for class 'fd_bayes_fit'
print(x, ...)

Arguments

Details

The posterior probability that a new observation belongs to class g is

P(g \mid \mathrm{sdp}_0) = \frac{f_g(\mathrm{sdp}_0)\,\pi_g} {\sum_{g'} f_{g'}(\mathrm{sdp}_0)\,\pi_{g'}}.

Value

An object of class "fd_bayes_fit" with components:

predicted

Predicted class labels.

prob

Matrix with columns prob_0 and prob_1: posterior probabilities.

log_odds

Log-odds \log(f_0\pi_0 / f_1\pi_1).

sd_train

The "fd_signed_depth" object for the training set.

Invisibly returns x.

Examples

set.seed(11)
M <- 80; N <- 100
t  <- seq(0, 1, length.out = M)
X0 <- t(replicate(50, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(50, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = 50)
fit <- bayes_depth_classify(X, y)
table(fit$predicted, y)

Fraiman-Muniz Functional Depth

Description

Computes the Fraiman-Muniz (FM) depth for a collection of discretised functional observations. Two variants are supported: the Tukey-FM depth, which maps depth values to [0, 1], and the Simplicial-FM depth, which maps to [0.5, 1]. The two are related by

\text{Tukey-FM}_i = 2\,(\text{Simplicial-FM}_i - 1/2).

Usage

fm_depth(X, grid = NULL, type = c("tukey", "simplicial"))

Arguments

Details

For a discretised dataset \{x_i(t_j)\}, i = 1,\ldots,N, j = 1,\ldots,M, the Simplicial-FM depth is

\text{FM}_i = \sum_{j=2}^{M}(t_j - t_{j-1}) \left[1 - \left|\frac{1}{2} - F_{N,t_j}(x_i(t_j))\right|\right],

where F_{N,t} is the empirical CDF of the sample at time t. The Tukey-FM depth substitutes the Tukey univariate depth \min\{F_{N,t}(x), 1 - F_{N,t}(x)\} at each time point.

Value

A numeric vector of length N containing the FM depth value for each curve.

References

Fraiman, R. and Muniz, G. (2001). Trimmed means for functional data. Test, 10(2), 419–440.

López-Pintado, S. and Romo, J. (2009). On the concept of depth for functional data. Journal of the American Statistical Association, 104(486), 718–734.

Examples

set.seed(1)
N <- 50; M <- 100
X <- matrix(rnorm(N * M), nrow = N)
d <- fm_depth(X)
plot(d, xlab = "Curve index", ylab = "Tukey-FM depth")

GAM Classifier on Signed Depth

Description

Fits a generalised additive model (GAM) with a smooth term on the signed depth to estimate class membership probabilities. An optional iterative outlier down-weighting scheme is available (Section 3.8 of Perez Ruiz, 2020).

Usage

gam_depth_classify(
  X_train,
  y_train,
  X_test = NULL,
  covariates = c("sdp", "sdp+mode"),
  k_gam = 10L,
  n_pc = 10L,
  downweight = FALSE,
  max_iter = 10L,
  grid = NULL,
  type = c("tukey", "simplicial")
)

## S3 method for class 'fd_gam_fit'
print(x, ...)

Arguments

Value

An object of class "fd_gam_fit" with components:

predicted

Predicted class labels.

prob

Matrix with columns prob_0 and prob_1.

gam_fit

The fitted gam object.

sd_train

The "fd_signed_depth" object for the training set.

covariates

Character: covariates used.

Invisibly returns x.

Examples

set.seed(17)
M <- 80; N <- 100
t  <- seq(0, 1, length.out = M)
X0 <- t(replicate(50, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(50, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = 50)
fit <- gam_depth_classify(X, y)
print(fit)

Cross-Validation for k-RNN Neighbourhood Size

Description

Selects the optimal half-neighbourhood size k by R-fold cross-validation, using either the minimum-error rule or the one-standard-error (1-SE) rule (Section 3.3.1 of Perez Ruiz, 2020).

Usage

krnn_cv(
  X,
  y,
  k_max = NULL,
  R = 10L,
  rule = c("min", "1se"),
  grid = NULL,
  type = c("tukey", "simplicial"),
  seed = NULL
)

## S3 method for class 'krnn_cv'
print(x, ...)

Arguments

Value

An object of class "krnn_cv" with components:

k_opt

Selected optimal k.

cv_error

Mean CV misclassification error for each k.

cv_se

Standard error of CV error per k.

k_max

Maximum k evaluated.

R

Number of folds used.

rule

The selection rule used.

Invisibly returns x.

Examples

set.seed(3)
M <- 80
t  <- seq(0, 1, length.out = M)
X0 <- t(replicate(50, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(50, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = 50)
cv <- krnn_cv(X, y, k_max = 20, R = 5, seed = 1)
print(cv)
plot_krnn_cv(cv)

k-Ranked Nearest Neighbour Classifier for Functional Data

Description

Fits the k-RNN classifier of Perez Ruiz (2020). Curves are ranked by their signed depth and each new observation is assigned to the majority class among its 2k nearest ranked neighbours (k above and k below).

Usage

krnn_fit(
  X_train,
  y_train,
  X_test = NULL,
  k = 5L,
  grid = NULL,
  type = c("tukey", "simplicial"),
  sd_train = NULL
)

## S3 method for class 'krnn_fit'
print(x, ...)

Arguments

Value

An object of class "krnn_fit" with components:

predicted

Integer vector of predicted class labels.

prob

Numeric vector of estimated probabilities \hat{P}(y=0|\mathrm{sdp}).

sd_train

The "fd_signed_depth" object for the training set.

y_train

The training labels.

k

The value of k used.

Invisibly returns x.

References

Perez Ruiz, D. A. (2020). Supervised Classification for Functional Data. PhD thesis, University of Manchester. Section 3.2.

See Also

krnn_cv, predict.krnn_fit

Examples

set.seed(7)
M <- 100; n <- 80
t <- seq(0, 1, length.out = M)
X0 <- t(replicate(n / 2, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(n / 2, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = n / 2)
idx_tr <- sample(n, 60)
fit <- krnn_fit(X[idx_tr, ], y[idx_tr], X[-idx_tr, ], k = 5)
print(fit)

k-RNN Moving-Average Smoother

Description

Estimates the conditional probability \hat{P}(y = 0 \mid \mathrm{sdp}) using the running-mean smoother (moving average) with span 2k. This is the regression interpretation of the k-RNN (Section 3.4 of Perez Ruiz, 2020).

Usage

krnn_smoother(
  X,
  y,
  k = 10L,
  grid_eval = NULL,
  boot = FALSE,
  B = 200L,
  alpha = 0.05,
  grid_fd = NULL,
  type = c("tukey", "simplicial")
)

Arguments

Value

An object of class "krnn_smoother" with components:

sdp_eval

Evaluation points (signed depths).

prob

Estimated conditional probabilities.

ci_lower, ci_upper

Bootstrap CI bounds (if boot = TRUE).

k

Half-neighbourhood size used.

Examples

set.seed(5)
M <- 100; N <- 80
t  <- seq(0, 1, length.out = M)
X0 <- t(replicate(40, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(40, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = 40)
sm <- krnn_smoother(X, y, k = 10)
plot_krnn_smoother(sm)

Logistic Regression Classifier on Signed Depth

Description

Fits a logistic regression model with class label as response and signed depth (and optionally signed distance to the mode) as covariates (Section 3.7 of Perez Ruiz, 2020).

Usage

logistic_depth_classify(
  X_train,
  y_train,
  X_test = NULL,
  model = c("sdp", "sdp+mode", "sdp*mode"),
  n_pc = 10L,
  grid = NULL,
  type = c("tukey", "simplicial")
)

## S3 method for class 'fd_logistic_fit'
print(x, ...)

Arguments

Details

Three model formulae are supported:

"sdp"

Model 1: signed depth only.

"sdp+mode"

Model 2: signed depth plus signed distance to mode.

"sdp*mode"

Model 3: Model 2 plus interaction term.

Value

An object of class "fd_logistic_fit" with components:

predicted

Predicted class labels.

prob

Matrix with columns prob_0 and prob_1.

glm_fit

The fitted glm object.

sd_train

The "fd_signed_depth" object for the training set.

model

Character: model formula used.

Invisibly returns x.

Examples

set.seed(13)
M <- 80; N <- 100
t  <- seq(0, 1, length.out = M)
X0 <- t(replicate(50, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(50, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = 50)
fit <- logistic_depth_classify(X, y, model = "sdp")
summary(fit$glm_fit)

Plot a krnn_cv Object

Description

Plots the cross-validation error curve against the neighbourhood size k, with standard-error bars and a vertical line at the selected optimum.

Usage

plot_krnn_cv(x, ...)

Arguments

Value

Invisibly returns x.

Examples

set.seed(3)
M <- 80
t  <- seq(0, 1, length.out = M)
X0 <- t(replicate(50, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(50, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = 50)
cv <- krnn_cv(X, y, k_max = 20, R = 5, seed = 1)
plot_krnn_cv(cv)

Plot a krnn_smoother Object

Description

Plots the estimated conditional probability \hat{P}(y = 0 \mid \mathrm{sdp}) against the signed depth, with optional bootstrap confidence bands.

Usage

plot_krnn_smoother(x, ...)

Arguments

Value

Invisibly returns x.

Examples

set.seed(5)
M <- 100; N <- 80
t  <- seq(0, 1, length.out = M)
X0 <- t(replicate(40, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(40, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = 40)
sm <- krnn_smoother(X, y, k = 10)
plot_krnn_smoother(sm)

Predict Method for krnn_fit Objects

Description

Classifies new functional observations using a fitted krnn_fit object.

Usage

## S3 method for class 'krnn_fit'
predict(object, newdata, y_true = NULL, ...)

Arguments

Value

A list with components predicted, prob, and (if y_true is supplied) misclass.

Examples

set.seed(7)
M <- 100; n <- 80
t <- seq(0, 1, length.out = M)
X0 <- t(replicate(n / 2, sin(2 * pi * t) + rnorm(M, sd = 0.4)))
X1 <- t(replicate(n / 2, cos(2 * pi * t) + rnorm(M, sd = 0.4)))
X  <- rbind(X0, X1)
y  <- rep(0:1, each = n / 2)
idx_tr <- sample(n, 60)
fit  <- krnn_fit(X[idx_tr, ], y[idx_tr], k = 5)
pred <- predict(fit, newdata = X[-idx_tr, ], y_true = y[-idx_tr])
pred$misclass

Reference (Median) Curve

Description

Returns the curve (or average of curves) that attains the maximum Fraiman-Muniz depth over the combined sample — the functional analogue of the median.

Usage

reference_curve(X, depth = NULL, ...)

Arguments

Value

A numeric vector of length M.

Examples

set.seed(1)
X <- matrix(rnorm(50 * 100), nrow = 50)
ref <- reference_curve(X)
plot(ref, type = "l", xlab = "t", ylab = "x(t)",
     main = "Reference (median) curve")

Signed Depth

Description

The core transformation of the k-RNN pipeline. For each curve computes

\mathrm{sdp}(x_i(t)) = \mathrm{sgn}\!\left\{\int_I(x_i(t)-x_{\mathrm{ref}}(t))\,dt\right\} \times D(x_i(t)),

where D(\cdot) is the Fraiman-Muniz depth and the sign is derived from the signed distance integral (equation 3.2 of Perez Ruiz, 2020). Curves above the reference receive a positive signed depth; curves below receive a negative one.

Usage

signed_depth(
  X,
  grid = NULL,
  type = c("tukey", "simplicial"),
  x_ref = NULL,
  depth = NULL
)

## S3 method for class 'fd_signed_depth'
print(x, ...)

Arguments

Value

An object of class "fd_signed_depth" with components:

sdp

Numeric vector of length N: the signed depths.

depth

Numeric vector of length N: raw FM depths.

sdi

Numeric vector of length N: signed distance integrals.

x_ref

Numeric vector of length M: reference curve.

grid

Numeric vector of length M: time grid used.

type

Character: depth variant used.

N

Number of curves.

M

Number of time points.

Invisibly returns x.

References

Perez Ruiz, D. A. (2020). Supervised Classification for Functional Data. PhD thesis, University of Manchester.

Examples

set.seed(42)
N <- 60; M <- 100
t  <- seq(0, 1, length.out = M)
X0 <- t(replicate(N / 2, sin(2 * pi * t) + rnorm(M, sd = 0.3)))
X1 <- t(replicate(N / 2, cos(2 * pi * t) + rnorm(M, sd = 0.3)))
X  <- rbind(X0, X1)
sd_obj <- signed_depth(X)
plot(sd_obj$sdp, col = rep(1:2, each = N / 2),
     xlab = "Curve index", ylab = "Signed depth",
     main = "Signed depth by group")
abline(h = 0, lty = 2)

Signed Distance Integral

Description

For each curve x_i(t), computes

\int_I \bigl(x_i(t) - x_{\mathrm{ref}}(t)\bigr)\,dt,

which is positive when the curve lies predominantly above the reference and negative when it lies below. This integral assigns a sign to the depth of each curve (equation 3.1 of Perez Ruiz, 2020).

Usage

signed_distance_integral(X, x_ref = NULL, grid = NULL)

Arguments

Value

A numeric vector of length N.

See Also

signed_depth, reference_curve

Examples

set.seed(1)
X <- matrix(rnorm(40 * 80), nrow = 40)
sdi <- signed_distance_integral(X)
hist(sdi, main = "Signed distance integrals", xlab = "SDI")

Simulate a Two-Group Functional Dataset

Description

Generates a labelled functional dataset from two Gaussian processes, useful for illustrating and testing the classifiers in fdclassify. The two groups differ in their mean function:

x_i(t) = \mu_g(t) + \varepsilon_i(t), \quad \varepsilon_i(t) \sim \mathcal{GP}(0, \sigma^2),

where \mu_0(t) = A_0 \sin(2\pi f_0 t) and \mu_1(t) = A_1 \cos(2\pi f_1 t) by default.

Usage

simulate_fd(
  n0 = 50L,
  n1 = 50L,
  M = 100L,
  sigma = 0.4,
  A0 = 1,
  A1 = 1,
  f0 = 1,
  f1 = 1,
  seed = NULL
)

Arguments

Value

A list with:

X

Numeric matrix (n_0+n_1) \times M.

y

Integer vector of labels (0 or 1).

grid

Numeric vector of length M on [0,1].

Examples

dat <- simulate_fd(n0 = 40, n1 = 40, seed = 1)
matplot(t(dat$X[dat$y == 0, ]), type = "l", col = "steelblue",
        lty = 1, xlab = "t", ylab = "x(t)", main = "Simulated curves")
matlines(t(dat$X[dat$y == 1, ]), col = "firebrick", lty = 1)
legend("topright", legend = c("Group 0", "Group 1"),
       col = c("steelblue","firebrick"), lty = 1)