risdr

risdr provides a reproducible framework for comparative sufficient dimension reduction with covariance regularisation and information-theoretic structural dimension selection. The current modelling interface supports continuous responses.

The package implements:

• sliced inverse regression (SIR);
• sliced average variance estimation (SAVE);
• directional regression (DR);
• principal Hessian directions (pHd);
• sample, ridge, Oracle Approximating Shrinkage (OAS), Ledoit-Wolf (LW), and Maximum Entropy Covariance (MEC) estimators;
• AIC, BIC, CAIC, ICOMP, and CICOMP structural dimension criteria;
• V-fold, repeated, and complexity-aware cross-validation;
• prediction diagnostics, subspace recovery measures, simulation utilities, and base R plotting methods.

Development status

Version 0.3.1 is the first CRAN release candidate for risdr. Version 0.3.0 remains the first public development release and is permanently archived on Zenodo. Version 0.3.1 has not yet been submitted to CRAN.

Installation

Install the package from a local source directory with:

install.packages("path/to/risdr", repos = NULL, type = "source")

During repository development, use:

devtools::install("path/to/risdr")

Install the current release candidate directly from GitHub with:

pak::pak("ilovemaths/risdr")

Minimal example

library(risdr)

set.seed(2026)

sim <- simulate_risdr_data(
  n = 160,
  p = 20,
  d = 2,
  rho = 0.6,
  model = "linear_quadratic",
  seed = 2026
)

fit <- fit_risdr(
  X = sim$X,
  y = sim$y,
  sdr_method = "dr",
  cov_method = "oas",
  nslices = 6,
  d_max = 6,
  selector = "cicomp"
)

fit
summary(fit)

prediction <- predict(fit, sim$X[1:10, , drop = FALSE])
evaluate_prediction(sim$y[1:10], prediction, d = fit$d)

Component-specific arguments are separated explicitly:

fit_ridge <- fit_risdr(
  X = sim$X,
  y = sim$y,
  sdr_method = "sir",
  cov_method = "ridge",
  d = 2,
  d_max = 4,
  cov_args = list(lambda = 0.15),
  stabilization_args = list(eps = 1e-7),
  sdr_args = list(slice_type = "quantile")
)

Structural dimension assessment

cv <- select_dimension_cv(
  X = sim$X,
  y = sim$y,
  sdr_method = "dr",
  cov_method = "oas",
  d_max = 5,
  v = 5,
  seed = 2026
)

cv$selected_d
cv$cv_table

See the package vignettes for the complete workflow, EPI case study, simulation design, covariance regularisation, and structural dimension selection.

Scope and reproducibility

All stochastic examples expose seeds. Training-set centring and scaling are stored in fitted objects and reused for prediction. The package records failed resampling fits rather than silently discarding them.

The supplied processed EPI training and test matrices are included under inst/extdata/epi, together with selected summary outputs from the completed thesis. The original single-file EPI corpus is not redistributed. Simulation fixtures are legacy records and must not be treated as newly validated results.

The corrected Simulation A, B1, and B2 workflow is configured in config.yml and can be run from the repository root with:

Rscript analysis/reproduce_simulations.R config.yml

Corrected files receive a _corrected_v0_3_0 suffix, so the workflow cannot overwrite the supplied legacy results.

Licence

risdr is released under GPL version 3 or later.