Package: voi 1.0.3

Simple example model | Specifying model inputs | Specifying model outputs | Expected value of perfect information | Computation using random sampling | Using the voi package to calculate EVPI | Analytic computation | Expected value of partial perfect information | Invoking the evppi function. | Changing the default calculation method | Gaussian process regression | Multivariate adaptive regression splines | INLA method | Bayesian additive regression trees (BART) | Tuning the generalized additive model method | Single-parameter methods | Traditional Monte Carlo nested loop method | Model evaluation function | Parameter simulation function | Invoking evppi_mc | Accounting for parameter correlation | Expected value of sample information | Function to generate study data | Built-in study designs | Importance sampling method | Moment matching method | Moment matching method: example using a built-in study design | Moment matching method: example using a custom study design | Value of Information in models for estimation | EVPI and EVPPI for estimation | How regression-based EVPPI estimation works | EVSI for estimation | Expected net benefit of sampling

Example EVSI dataset | EVSI curves | Expected net benefit of sampling | Optimal sample size | Smooth interpolation | Note about uncertainty | Curve of optimal sample size | Probability of a cost-effective trial