L1 minimization (Linear Programming) | Parametric Optimization with the Update-Solve Loop

Introduction | The global problem and its consensus split | The federated fit in the clear | The same fit under threshold FHE | Comparison with the centralized fit | What the protocol hides and reveals | Discussion | Limitations

The statistical problem | The privacy constraint | The protocol | Implementation | 1. Generate a CKKS key pair | 2. Build workers and master | 3. Run mle() through the encrypted channel | Beyond MLE | CAVEAT

The statistical problem | DLBCL lymphoma cohort | The aggregated fit | The protocol | Wiring up the protocol | Iterative MLE through the encrypted protocol | Comparison with the cleartext fit | What just happened | What this demonstrates | Caveats and extensions

Introduction | The setup | Threat model | The protocol | Synthetic data | Threshold key generation | Joint rotation keys | Per-site adapter fit and database setup | Diagonal-encoded matrix-vector multiply | Inner product against the local database | What is in the slots we do not read | Site contribution_fn: full per-site protocol step | Master orchestration and threshold-decrypted top-k | Reference-truth comparison | What the adapter buys: fidelity, posture, and a sweet spot | Discussion | Limitations | Related work | References

Introduction | The problem and its consensus split | Adding noise | A brief output-DP primer | The protocol modification | The stopping rule changes | Setup | Simulated cohort | Choosing $\rho$ and $T$ without touching the cohort | Threshold-FHE setup | The DP-ADMM loop | Centralized CVXR fit | The $\sigma$ sweep | Privacy budget | References

The problem this package addresses | Terms you will meet | How the pieces fit together | Sites that are not in this R session | What "the right answer" means | Which vignette to read | A note on Paillier | References

Introduction | A brief output-DP primer | The protocol | The Cox setup (same DLBCL data as the other Cox vignettes) | Threshold setup and DP-noised workers | Mechanical correctness: $\sigma = 0$ reproduces cox-threshold | Increasing noise: where does it break? | Nelder–Mead at the same noise scales | Privacy budget | References

Introduction | Threat model | The Cox setup (same DLBCL data as cox.Rmd) | The protocol | Implementation | Iterative MLE through the threshold protocol | Comparison with the cleartext fit | Discussion | Limitations

The problem | The target answer | Why the classic solution needed two non-cooperating parties | Threshold encryption removes the need for masking | The threshold-BFV implementation | The encrypted answer is exact | Summary

Introduction | Exact, for integer schemes | Approximate, for CKKS

The use case | Scenario: weighted biomarker score | Setup: hospital's context and patient data | Lab side: apply the model to encrypted data | Hospital side: decrypt the results | Verification | Transport guarantees | Threat model: model extraction from the hospital side | Layered defenses | Network protocol: serialization

Overview | The setting | Step 1: train in the clear | Step 2: encrypt patient data | Step 3: evaluate the linear predictor (encrypted) | Step 4: apply the sigmoid (encrypted) | Step 5: decrypt predictions | Summary | Limitations

The problem | Setup | The aggregator sets up encryption | Each site computes locally and encrypts | The aggregator aggregates | Verification | The details of what happened | Serializing for a real distributed protocol

Introduction | Your first integral | The unified cubintegrate() interface | Choosing a method | Vector-valued integrands | Robustness: when hcubature and pcubature can silently fail | The opt-in robust path | When robust is not enough: the sampling-density cliff | The NOT_CONVERGED return code | Where to go next | Implementation notes | Session info

When to read this | A timing harness | Example 1 | Multivariate normal | Product of cosines | Gaussian function | Discontinuous function | A simple polynomial (product of coordinates) | Gaussian centered at $\frac{1}{2}$ | Double Gaussian | Tsuda's example | Morokoff & Caflisch example | Wang-Landau sampling 1d, 2d examples | Summary | Session info

Introduction | A Nonparametric Example | Tidy output | Visualization | Acceleration methods | A Parametric Example | A Logistic Model | A Penalized Logistic Model | Ratio of Independent Variance Estimates | Migration from pre-1.0 API | Function mapping | Parameter mapping | Return structure | Deprecation timeline | References

Introduction | Table 1 Results | Scenario S0 | Alternative Scenario S1 | Alternative Scenario S2 | Alternative Scenario S3 | Alternative Scenario S4 | Alternative Scenario S5 | Alternative Scenario S6 | Alternative Scenario S7 | Alternative Scenario S8 | Alternative Scenario S9 | Alternative Scenario S10 | Table 2 Results | References

Continuous Rankin Simulations | The NULL Scenario | The ALT1 Scenario | The ALT2 Scenario | Discrete Rankin Simulations | The Discretized Scenarios | References

Introduction | Scenario S0

Introduction | An Example | Stratified Cox Model | Session Info

A simulation example | The Shakespeare data | Bootstrap Comparison | Predict ratio of new distinct words | Normal Example | Another Normal example (Twin Towers) | Binomial Example | Posterior Estimates

Description and Usage | Arguments | Value | Simulation Example