Regularised and Information-Theoretic Sufficient Dimension Reduction

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Documentation for package ‘risdr’ version 0.4.0

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

risdr-package	risdr: Regularised and Information-Theoretic Sufficient Dimension Reduction
adjusted_r_squared	Adjusted coefficient of determination
ar1_covariance	AR(1) covariance matrix
C1F	C1F complexity and induced adaptive weights
choose_dimension	Choose dimension from criteria table
compute_dr	Compute Directional Regression
compute_ic	Compute one reduced-model information criterion
compute_ic_values	Compute all reduced-model information criteria
compute_information_criteria	Compute model selection criteria
compute_penalty_weights	Compute adaptive penalty weights from C1F variable contributions
compute_phd	Compute Principal Hessian Directions
compute_save	Compute Sliced Average Variance Estimation
compute_scores	Compute SDR scores for new data
compute_sdr	General SDR kernel dispatcher
compute_sir	Compute Sliced Inverse Regression
cov_complexity_C1	Covariance complexity C1
cov_complexity_C1F	Scale-invariant covariance complexity C1F
cov_condition_number	Covariance condition number
cov_diagnostics	Covariance eigenvalue summary
cov_effective_rank	Effective covariance rank
cov_lw	Ledoit-Wolf type covariance estimator
cov_mec	Maximum Entropy Covariance estimator
cov_oas	Oracle Approximating Shrinkage covariance estimator
cov_ridge	Ridge-type covariance estimator
cov_sample	Sample covariance matrix
criterion_weights	Information criterion weights
dual_pca_scores	Dual PCA score construction for p much larger than n
estimate_cov	General covariance estimator dispatcher
evaluate_prediction	Evaluate predictions
evaluate_prediction_cv	Repeated cross-validation for predictive assessment
extract_loadings	Extract SDR loadings
filter_low_variance	Filter low-variance predictors
fit_downstream_lm	Fit downstream regression model on SDR scores
fit_risdr	Fit regularised and information-theoretic SDR model
fit_risdr_dual	Fit dual high-dimensional RISDR
fit_risdr_realdata	Fit RISDR to real high-dimensional data
fit_risdr_sparse	Fit regularised and information-theoretic sufficient dimension reduction
fit_risdr_sparse_realdata	Fit RISDR to real high-dimensional data
fit_sparse_grid	Fit sparse SDR directions over a lambda grid
mae	Mean absolute error
make_cv_folds	Create cross-validation folds
make_slices	Create response slices
mape	Mean absolute percentage error
plot_cv_dimension	Plot cross-validation dimension selection result
plot_dimension_selection	Plot dimension selection criteria
plot_ladle	Plot ladle dimension diagnostic
plot_loadings	Plot SDR loadings
plot_prediction	Prediction plot
plot_residuals	Residual plot
plot_scree	Scree plot of SDR eigenvalues
plot_sufficient	Sufficient summary plot
plot_sufficient2d	Two-direction sufficient summary plot
predict.risdr	Predict method for risdr objects
predict.risdr_dual	Predict from the sparse or dual SDR engine
predict.risdr_sparse	Predict from the sparse or dual SDR engine
prediction_correlation	Prediction correlation
predict_downstream_lm	Predict from downstream SDR regression model
prepare_survival_response	Prepare survival response
print.risdr	Print risdr object
print.risdr_dual	Print method for dual RISDR objects
print.risdr_realdata	Print real-data RISDR workflow
print.risdr_sparse	Print risdr object
print.risdr_sparse_realdata	Print real-data RISDR workflow
print.summary.risdr	Print summary of risdr object
print.summary.risdr_sparse	Print summary of risdr object
projection_matrix	Projection matrix
risdr	risdr: Regularised and Information-Theoretic Sufficient Dimension Reduction
rmse	Root mean squared error
run_one_simulation	Run one SDR simulation replication
run_risdr_simulation	Run SDR simulation study
r_squared	Coefficient of determination
select_dimension	Select structural dimension
select_dimension_cv	Cross-validation dimension selection for SDR
select_dimension_cv_icomp	Complexity-aware cross-validation for structural dimension selection
select_dimension_ladle	Ladle-type structural dimension diagnostic
simulate_risdr_data	Simulate SDR data
slice_covariances	Compute slice covariance matrices
slice_means	Compute slice means
slice_proportions	Compute slice proportions
slice_response	Slice response for inverse regression
slice_summary	Summarise response slices
stabilize_cov	General covariance stabilisation dispatcher
stabilize_eigenfloor	Eigenvalue floor stabilisation
stabilize_nearest_pd	Nearest positive definite stabilisation
stabilize_ridge	Ridge stabilisation of covariance matrix
subspace_distance	Subspace distance
summarise_simulation	Summarise simulation results
summarise_sparsity	Summarise sparsity pattern
summary.risdr	Summarise risdr object
summary.risdr_dual	Summary method for dual RISDR objects
summary.risdr_sparse	Summarise risdr object
weighted_soft_threshold	Weighted soft-threshold operator
weighted_sparsify_directions	Weighted sparse directions from dense SDR directions