risdr: Regularised and Information-Theoretic Sufficient Dimension Reduction

The package implements SIR, SAVE, DR, and pHd for continuous responses, together with covariance regularisation, information-theoretic structural dimension selection, prediction, resampling, simulation, and diagnostic utilities.

Main interface

Use fit_risdr() to estimate an SDR model and predict.risdr() to obtain predictions for new observations. Use select_dimension_cv(), select_dimension_cv_icomp(), or select_dimension_ladle() for complementary structural-dimension diagnostics.

Scope

fit_risdr() retains its continuous-response scope. Use fit_risdr_sparse() for joint dimension and sparsity selection with continuous, categorical, and right-censored survival responses, or fit_risdr_dual() for sample-space PCA reduction when predictors outnumber observations. fit_risdr_sparse_realdata() adds variance screening. See the sparse-survival vignette for the frozen thesis conventions.

See also

Useful links:

• https://github.com/ilovemaths/risdr

• https://ilovemaths.github.io/risdr/

• Report bugs at https://github.com/ilovemaths/risdr/issues

Author

Maintainer: Kabir Olorede kabirolorede@gmail.com