R: Estimation of Optimal Size for a Holdout Set for Updating a Predictive Score

# Estimation of Optimal Size for a Holdout Set for Updating a Predictive Score

## Documentation for package ‘OptHoldoutSize’ version 0.1.0.2

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

## Help Pages

add_aspre_interactions | Add interaction terms corresponding to ASPRE model
aspre | Computes ASPRE score
aspre_emulation | Emulation-based OHS estimation for ASPRE
aspre_k2 | Cost estimating function in ASPRE simulation
aspre_parametric | Parametric-based OHS estimation for ASPRE
ci_cover_a_yn | Data for example on asymptotic confidence interval for OHS.
ci_cover_cost_a_yn | Data for example on asymptotic confidence interval for min cost.
ci_cover_cost_e_yn | Data for example on empirical confidence interval for min cost.
ci_cover_e_yn | Data for example on empirical confidence interval for OHS.
ci_mincost | Confidence interval for minimum total cost, when estimated using parametric method
ci_ohs | Confidence interval for optimal holdout size, when estimated using parametric method
cov_fn | Covariance function for Gaussian process
data_example_simulation | Data for vignette showing general example
data_nextpoint_em | Data for 'next point' demonstration vignette on algorithm comparison using emulation algorithm
data_nextpoint_par | Data for 'next point' demonstration vignette on algorithm comparison using parametric algorithm
error_ohs_emulation | Measure of error for emulation-based OHS emulation
exp_imp_fn | Expected improvement
gen_base_coefs | Coefficients for imperfect risk score
gen_preds | Generate matrix of random observations
gen_resp | Generate response
grad_mincost_powerlaw | Gradient of minimum cost (power law)
grad_nstar_powerlaw | Gradient of optimal holdout size (power law)
logistic | Logistic
logit | Logit
model_predict | Make predictions
model_train | Train model (wrapper)
mu_fn | Updating function for mean.
next_n | Finds best value of n to sample next
ohs_array | Data for vignette on algorithm comparison
ohs_resample | Data for vignette on algorithm comparison
optimal_holdout_size | Estimate optimal holdout size under parametric assumptions
optimal_holdout_size_emulation | Estimate optimal holdout size under semi-parametric assumptions
oracle_pred | Generate responses
params_aspre | Parameters of reported ASPRE dataset
plot.optholdoutsize | Plot estimated cost function
plot.optholdoutsize_emul | Plot estimated cost function using emulation (semiparametric)
powerlaw | Power law function
powersolve | Fit power law curve
powersolve_general | General solver for power law curve
powersolve_se | Standard error matrix for learning curve parameters (power law)
psi_fn | Updating function for variance.
sens10 | Sensitivity at theshold quantile 10%
sim_random_aspre | Simulate random dataset similar to ASPRE training data
split_data | Split data