Type: Package
Title: Fast and Scalable Cellwise-Robust Ensemble
Version: 2.0.0
Date: 2026-03-01
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>
Description: Functions to perform robust variable selection and regression using the Fast and Scalable Cellwise-Robust Ensemble (FSCRE) algorithm. The approach establishes a robust foundation using the Detect Deviating Cells (DDC) algorithm and robust correlation estimates. It then employs a competitive ensemble architecture where a robust Least Angle Regression (LARS) engine proposes candidate variables and cross-validation arbitrates their assignment. A final robust MM-estimator is applied to the selected predictors.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
Biarch: true
Imports: cellWise, robustbase, mvnfast
Suggests: testthat
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-03-01 19:08:10 UTC; anthony
Author: Anthony Christidis [aut, cre], Gabriela Cohen-Freue [aut]
Repository: CRAN
Date/Publication: 2026-03-02 06:10:13 UTC

Check Input Data for srlars Function

Description

Internal helper function to validate arguments passed to srlars. Checks types, dimensions, and logical constraints.

Usage

checkInputData(x, y, n_models, tolerance, max_predictors, robust, compute_coef)

Arguments

x

Design matrix.

y

Response vector.

n_models

Number of models in the ensemble.

tolerance

Relative improvement tolerance for stopping.

max_predictors

Maximum total number of variables to select.

robust

Logical.

compute_coef

Logical.

Value

NULL. Stops execution with an error message if invalid inputs are detected.

Coefficients for srlars Object

Description

coef.srlars returns the averaged coefficients for a srlars object.

Usage

## S3 method for class 'srlars'
coef(object, model_index = NULL, ...)

Arguments

object

An object of class srlars.

model_index

Indices of the sub-models to include in the ensemble average. Default is NULL, which includes all models.

...

Additional arguments for compatibility.

Value

A numeric vector containing the averaged intercept (first element) and slope coefficients.

Author(s)

Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca

See Also

srlars, predict.srlars

Examples

# Required libraries
library(mvnfast)
library(cellWise)
library(robustbase)

# Simulation parameters
n <- 50
p <- 100
rho.within <- 0.8
rho.between <- 0.2
p.active <- 20
group.size <- 5
snr <- 3
contamination.prop <- 0.1

# Setting the seed
set.seed(0)

# Block correlation structure
sigma.mat <- matrix(0, p, p)
sigma.mat[1:p.active, 1:p.active] <- rho.between
for(group in 0:(p.active/group.size - 1))
  sigma.mat[(group*group.size+1):(group*group.size+group.size),
  (group*group.size+1):(group*group.size+group.size)] <- rho.within
diag(sigma.mat) <- 1

# Simulation of beta vector
true.beta <- c(runif(p.active, 0, 5)*(-1)^rbinom(p.active, 1, 0.7), rep(0, p - p.active))

# Setting the SD of the variance
sigma <- as.numeric(sqrt(t(true.beta) %*% sigma.mat %*% true.beta)/sqrt(snr))

# Simulation of uncontaminated data
x <- mvnfast::rmvn(n, mu = rep(0, p), sigma = sigma.mat)
y <- x %*% true.beta + rnorm(n, 0, sigma)

# Cellwise contamination
contamination_indices <- sample(1:(n * p), round(n * p * contamination.prop))
x_train <- x
x_train[contamination_indices] <- runif(length(contamination_indices), -10, 10)

# FSCRE Ensemble model
ensemble_fit <- srlars(x_train, y,
                       n_models = 5,
                       tolerance = 0.01,
                       robust = TRUE,
                       compute_coef = TRUE)

# Ensemble coefficients
# Default: Average over all models
ensemble_coefs <- coef(ensemble_fit)

# Sensitivity (Recall)
active_selected <- which(ensemble_coefs[-1] != 0)
true_active <- which(true.beta != 0)
recall <- length(intersect(active_selected, true_active)) / length(true_active)
print(paste("Recall:", recall))

# Precision
if(length(active_selected) > 0){
  precision <- length(intersect(active_selected, true_active)) / length(active_selected)
} else {
  precision <- 0
}
print(paste("Precision:", precision))

Compute Cross-Validated Prediction Error (Internal)

Description

Computes the V-fold CV error for a linear regression model using fast LS fitting.

Usage

computeCVError(x, y, active_set, folds = 5)

Arguments

x

Design matrix (imputed).

y

Response vector (imputed).

active_set

Indices of predictors to include.

folds

Number of folds (default 5).

Value

A single numeric value (Mean Squared Prediction Error).

FSCRE Final Robust Fitting (Internal)

Description

Implements Stage 3: Fitting robust MM-estimators to the selected variable sets.

Usage

computeFinalFit(x.imp, y.imp, active.sets, compute_coef)

Arguments

x.imp

Imputed design matrix.

y.imp

Imputed response vector.

active.sets

List of integer vectors (indices of selected variables).

compute_coef

Logical.

Value

A list containing 'coefficients' (list of vectors) and 'intercepts' (vector).

Compute Robust Foundation (Internal)

Description

Implements Stage 1 of the FSCRE algorithm: data cleaning via DDC and robust correlation estimation.

Usage

computeRobustFoundation(x, y, robust)

Arguments

x

Design matrix.

y

Response vector.

robust

Logical.

Value

A list containing the imputed data (x.imp, y.imp), the robust correlation structures (Rx, ry), and the DDC object for prediction.

Get Robust LARS Proposal (Internal)

Description

Calculates the next LARS step analytically using correlation matrices.

Usage

getLarsProposal(
  Rx,
  active.set,
  sign.vector,
  current.correlations,
  available.vars
)

Arguments

Rx

Global correlation matrix (p x p).

active.set

Integer vector of active indices.

sign.vector

Integer vector of signs for active set.

current.correlations

Numeric vector of current correlations with residual.

available.vars

Integer vector of candidate indices to search.

Value

A list containing: next_var, next_sign, gamma, a_vec (for update). Or NULL.

FSCRE Competitive Selection Loop (Internal)

Description

Implements Stage 2 of the FSCRE algorithm: the iterative competitive selection.

Usage

performSelectionLoop(Rx, ry, x.imp, y.imp, n_models, max_predictors, tolerance)

Arguments

Rx

Global robust predictor correlation matrix.

ry

Global robust predictor-response correlation vector.

x.imp

Imputed design matrix (for CV).

y.imp

Imputed response vector (for CV).

n_models

Number of models (K).

max_predictors

Maximum total predictors to select.

tolerance

Stopping tolerance.

Value

A list containing the final active sets.

Predictions for srlars Object

Description

predict.srlars returns the predictions for a srlars object.

Usage

## S3 method for class 'srlars'
predict(object, newx, model_index = NULL, dynamic = TRUE, ...)

Arguments

object

An object of class srlars.

newx

New data matrix for predictions.

model_index

Indices of the sub-models to include in the ensemble. Default is NULL (all models).

dynamic

Logical. If TRUE, and the model was trained robustly, the new data newx is cleaned using DDCpredict before prediction. This ensures consistency with the robust training phase. Default is TRUE.

...

Additional arguments for compatibility.

Value

A numeric vector of predictions.

Author(s)

Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca

See Also

srlars

Examples

# Required libraries
library(mvnfast)
library(cellWise)
library(robustbase)

# Simulation parameters
n <- 50
p <- 100
rho.within <- 0.8
rho.between <- 0.2
p.active <- 20
group.size <- 5
snr <- 3
contamination.prop <- 0.1

# Setting the seed
set.seed(0)

# Block correlation structure
sigma.mat <- matrix(0, p, p)
sigma.mat[1:p.active, 1:p.active] <- rho.between
for(group in 0:(p.active/group.size - 1))
  sigma.mat[(group*group.size+1):(group*group.size+group.size),
  (group*group.size+1):(group*group.size+group.size)] <- rho.within
diag(sigma.mat) <- 1

# Simulation of beta vector
true.beta <- c(runif(p.active, 0, 5)*(-1)^rbinom(p.active, 1, 0.7), rep(0, p - p.active))

# Setting the SD of the variance
sigma <- as.numeric(sqrt(t(true.beta) %*% sigma.mat %*% true.beta)/sqrt(snr))

# Simulation of uncontaminated data
x <- mvnfast::rmvn(n, mu = rep(0, p), sigma = sigma.mat)
y <- x %*% true.beta + rnorm(n, 0, sigma)

# Cellwise contamination
contamination_indices <- sample(1:(n * p), round(n * p * contamination.prop))
x_train <- x
x_train[contamination_indices] <- runif(length(contamination_indices), -10, 10)

# FSCRE Ensemble model
ensemble_fit <- srlars(x_train, y,
                       n_models = 5,
                       tolerance = 0.01,
                       robust = TRUE,
                       compute_coef = TRUE)

# Simulation of test data
m <- 50
x_test <- mvnfast::rmvn(m, mu = rep(0, p), sigma = sigma.mat)
y_test <- x_test %*% true.beta + rnorm(m, 0, sigma)

# Prediction of test samples
# Default: Averaged prediction from all models
# Dynamic imputation is used by default if the model is robust
ensemble_preds <- predict(ensemble_fit, newx = x_test)

# Evaluate MSPE
mspe <- mean((y_test - ensemble_preds)^2) / sigma^2
print(paste("MSPE:", mspe))

Fast and Scalable Cellwise-Robust Ensemble (FSCRE)

Description

srlars performs the FSCRE algorithm for robust variable selection and regression.

Usage

srlars(
  x,
  y,
  n_models = 5,
  tolerance = 1e-08,
  max_predictors = NULL,
  robust = TRUE,
  compute_coef = TRUE
)

Arguments

x

Design matrix (n x p).

y

Response vector (n x 1).

n_models

Number of models in the ensemble (K). Default is 5.

tolerance

Relative improvement tolerance for stopping (tau). Default is 1e-8.

max_predictors

Maximum total number of variables to select across all models. Default is n * n_models.

robust

Logical. If TRUE (default), performs DDC imputation and robust initialization.

compute_coef

Logical. If TRUE, fits the final robust MM-models. Default is TRUE.

Value

An object of class srlars containing the selected variables and coefficients.

Author(s)

Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca

See Also

coef.srlars, predict.srlars

Examples

# Required libraries
library(mvnfast)
library(cellWise)
library(robustbase)

# Simulation parameters
n <- 50
p <- 100
rho.within <- 0.8
rho.between <- 0.2
p.active <- 20
group.size <- 5
snr <- 3
contamination.prop <- 0.1

# Setting the seed
set.seed(0)

# Block correlation structure
sigma.mat <- matrix(0, p, p)
sigma.mat[1:p.active, 1:p.active] <- rho.between
for(group in 0:(p.active/group.size - 1))
  sigma.mat[(group*group.size+1):(group*group.size+group.size),
  (group*group.size+1):(group*group.size+group.size)] <- rho.within
diag(sigma.mat) <- 1

# Simulation of beta vector
true.beta <- c(runif(p.active, 0, 5)*(-1)^rbinom(p.active, 1, 0.7), rep(0, p - p.active))

# Setting the SD of the variance
sigma <- as.numeric(sqrt(t(true.beta) %*% sigma.mat %*% true.beta)/sqrt(snr))

# Simulation of uncontaminated data
x <- mvnfast::rmvn(n, mu = rep(0, p), sigma = sigma.mat)
y <- x %*% true.beta + rnorm(n, 0, sigma)

# Cellwise contamination
contamination_indices <- sample(1:(n * p), round(n * p * contamination.prop))
x_train <- x
x_train[contamination_indices] <- runif(length(contamination_indices), -10, 10)

# FSCRE Ensemble model
ensemble_fit <- srlars(x_train, y,
                       n_models = 5,
                       tolerance = 1e-8,
                       robust = TRUE,
                       compute_coef = TRUE)

# Check selected variables
print(ensemble_fit$active.sets)