Package index • SurvBregDiv

Low-Dimensional Individual-Level Internal–External Integration

Models that integrate individual-level external data via composite likelihood weighting, for both full-cohort Cox and nested case-control (NCC) designs.

cox_indi(): Cox Proportional Hazards Model Integrated with External Individual-level Information
cv.cox_indi(): Cross-Validated cox_indi to Tune etas
ncc_indi(): Conditional Logistic Regression with Individual-level External Data (CLR-Indi)
cv.ncc_indi(): Cross-Validated CLR with Individual-Level External Data

Low-Dimensional KL Divergence-Based Integration

Models that integrate external coefficient summaries via Kullback–Leibler divergence penalization, for both full-cohort Cox and nested case-control (NCC) designs.

coxkl(): Cox Proportional Hazards Model with KL Divergence for Data Integration
cv.coxkl(): Cross-Validated Cox–KL to Tune the Integration Parameter (eta)
bopt.coxkl(): Bayesian Optimization for the Cox–KL Integration Parameter (eta)
coxkl_ties(): Cox Proportional Hazards Model with KL Divergence for Data Integration (Ties Handling)
cv.coxkl_ties(): Cross-Validated Cox–KL with Ties Handling to Tune the Integration Parameter (eta)
ncckl(): Conditional Logistic Regression with KL Divergence (CLR-KL)
cv.ncckl(): Cross-Validated Conditional Logistic Regression with KL Integration

Low-Dimensional Mahalanobis Distance-Based Integration

Models that integrate external coefficient and curvature summaries via Mahalanobis distance penalization, for both full-cohort Cox and matched case-control (NCC) designs.

cox_MDTL(): Cox Proportional Hazards Model with Mahalanobis Distance Transfer Learning
cv.cox_MDTL(): Cross-Validation for Cox MDTL Model
ncc_MDTL(): Conditional Logistic Regression with Mahalanobis Distance Transfer Learning (CLR-MDTL)
cv.ncc_MDTL(): Cross-Validated CLR with Mahalanobis Distance Transfer Learning

High-Dimensional Individual-Level Internal–External Integration

Penalized models with Elastic Net regularization that integrate individual-level external data, for both full-cohort Cox and nested case-control (NCC) designs.

cox_indi_enet(): Cox Proportional Hazards Model Integrated with External Individual-level Data and Elastic Net Penalty
cv.cox_indi_enet(): Cross-Validation for Cox Model Integrated with External Individual-level Data and Elastic Net Penalty
ncc_indi_enet(): Conditional Logistic Regression with Individual-level External Data and Elastic Net Penalty (CLR-Indi-ENet)
cv.ncc_indi_enet(): Cross-Validated CLR with Individual-Level External Data and Elastic Net Penalty

High-Dimensional KL Divergence-Based Integration

Penalized models with Ridge, Lasso, or Elastic Net regularization that integrate external coefficient summaries via KL divergence, for both full-cohort Cox and nested case-control (NCC) designs.

coxkl_ridge(): Cox Proportional Hazards Model with Ridge Penalty and External Information
coxkl_enet(): Cox Proportional Hazards Model with KL Divergence and Elastic Net Penalty
cv.coxkl_ridge(): Cross-Validation for CoxKL Ridge Model (Tuning Eta and Lambda)
cv.coxkl_enet(): Cross-Validation for CoxKL Model with Elastic Net & Lasso Penalty
ncckl_enet(): Conditional Logistic Regression with KL Divergence and Elastic Net Penalty (CLR-KL-ENet)
cv.ncckl_enet(): Cross-Validated CLR-KL with Elastic Net Penalty

High-Dimensional Mahalanobis Distance-Based Integration

Penalized models with Ridge, Lasso, or Elastic Net regularization that integrate external coefficient and curvature summaries via Mahalanobis distance, for both full-cohort Cox and nested case-control (NCC) designs.

cox_MDTL_ridge(): Cox MDTL with Ridge Regularization
cox_MDTL_enet(): Fit Cox Model with Multi-Domain Transfer Learning and Elastic Net Penalty
cv.cox_MDTL_ridge(): Cross-Validation for Cox MDTL with Ridge Regularization
cv.cox_MDTL_enet(): Cross-Validation for Cox MDTL with Elastic Net Regularization
ncc_MDTL_enet(): Conditional Logistic Regression with Mahalanobis Distance Transfer Learning and Elastic Net (CLR-MDTL-ENet)
cv.ncc_MDTL_enet(): Cross-Validated CLR with Mahalanobis Distance Transfer Learning and Elastic Net Penalty

Variable Selection and Stability for High-Dimensional Models

variable_importance(): Bootstrap Variable Importance via Selection Frequency
coxkl_enet.StabSelect(): Stability Selection for KL-Integrated Cox Elastic-Net Models
cox_MDTL_enet.StabSelect(): Stability Selection for MDTL-Integrated Cox Elastic-Net Models

Bagging for High-Dimensional Models

coxkl_enet_bagging(): Bagging for KL-Integrated Cox Elastic-Net Models
cox_MDTL_enet_bagging(): Bagging for MDTL-Integrated Cox Elastic-Net Models

Multi-Source Integration

coxkl_enet.multi(): Multi-Source Integration for KL-Integrated Cox Elastic-Net Models

Plotting Functions

cv.plot(): Plot Cross-Validation Results vs Eta
plot(<coxkl>): Plot Validation Results for coxkl Object
plot(<coxkl_ridge>): Plot Validation Results for coxkl_ridge Object
plot(<coxkl_enet>): Plot Validation Results for coxkl_enet Object
plot(<cox_MDTL>): Plot Validation Results for Cox_MDTL Object
plot(<cox_MDTL_enet>): Plot Validation Results for cox_MDTL_enet Object
plot(<cox_MDTL_ridge>): Plot Validation Results for cox_MDTL_ridge Object
plot(<StabSelect>): Plot Stability Selection Path
plot(<variable_importance>): Plot Variable Importance (Selection Frequency)
plot(<coxkl_enet.multi>): Plot Method for Multi-Source KL-Integrated Cox Elastic-Net Models

Utilities

generate_eta(): Generate a Sequence of Tuning Parameters (eta)

Datasets

ExampleData_lowdim: Example low-dimensional survival data
ExampleData_highdim: Example high-dimensional survival data
ExampleData_cc: Example Data for Conditional Logistic Regression
ExampleData_cc_highdim: Example high-dimensional matched case-control data
ExampleData_indi: Example internal/external Cox individual-level data