# Bandit-Based Experiments and Policy Evaluation

## Documentation for package ‘banditsCI’ version 1.0.0

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

## Help Pages

.check_A | Check Number of Observations for Inference
.check_first_batch | Check First Batch Validity
.check_shape | Check Shape Compatibility of Probability Objects
aw_estimate | Estimate policy value via non-contextual adaptive weighting.
aw_scores | Compute AIPW/doubly robust scores.
aw_var | Variance of policy value estimator via non-contextual adaptive weighting.
calculate_balwts | Calculate balancing weight scores.
calculate_continuous_X_statistics | Estimate/variance of policy evaluation via contextual weighting.
draw_thompson | Thompson Sampling draws.
estimate | Estimate/variance of policy evaluation via non-contextual weighting.
generate_bandit_data | Generate classification data.
ifelse_clip | Clip lamb values between a minimum x and maximum y.
impose_floor | Impose probability floor.
LinTSModel | Linear Thompson Sampling model.
output_estimates | Policy evaluation with adaptively generated data.
plot_cumulative_assignment | Plot cumulative assignment for bandit experiment.
ridge_init | Ridge Regression Initialization for Arm Expected Rewards
ridge_muhat_lfo_pai | Leave-future-out ridge-based estimates for arm expected rewards.
ridge_update | Updates ridge regression matrices.
run_experiment | Run an experiment using Thompson Sampling.
simple_tree_data | Generate simple tree data.
stick_breaking | Stick breaking function.
twopoint_stable_var_ratio | Calculate allocation ratio for a two-point stable-variance bandit.
update_thompson | Update linear Thompson Sampling model.