# Cram Method for Efficient Simultaneous Learning and Evaluation

## Documentation for package ‘cramR’ version 0.1.1

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

## Help Pages

BatchContextualEpsilonGreedyPolicy | Batch Contextual Epsilon-Greedy Policy
BatchContextualLinTSPolicy | Batch Contextual Thompson Sampling Policy
BatchLinUCBDisjointPolicyEpsilon | Batch Disjoint LinUCB Policy with Epsilon-Greedy
ContextualLinearBandit | Contextual Linear Bandit Environment
cram_bandit | Cram Bandit: On-policy Statistical Evaluation in Contextual Bandits
cram_bandit_est | Cram Bandit Policy Value Estimate
cram_bandit_sim | Cram Bandit Simulation
cram_bandit_var | Cram Bandit Variance of the Policy Value Estimate
cram_estimator | Cram Policy Estimator for Policy Value Difference (Delta)
cram_expected_loss | Cram ML Expected Loss Estimate
cram_learning | Cram Policy Learning
cram_ml | Cram ML: Simultaneous Machine Learning and Evaluation
cram_policy | Cram Policy: Efficient Simultaneous Policy Learning and Evaluation
cram_policy_value_estimator | Cram Policy: Estimator for Policy Value (Psi)
cram_simulation | Cram Policy Simulation
cram_variance_estimator | Cram Policy: Variance Estimate of the crammed Policy Value Difference (Delta)
cram_variance_estimator_policy_value | Cram Policy: Variance Estimate of the crammed Policy Value estimate (Psi)
cram_var_expected_loss | Cram ML: Variance Estimate of the crammed expected loss estimate
fit_model | Cram Policy: Fit Model
fit_model_ml | Cram ML: Fit Model ML
get_betas | Generate Reward Parameters for Simulated Linear Bandits
LinUCBDisjointPolicyEpsilon | LinUCB Disjoint Policy with Epsilon-Greedy Exploration
ml_learning | Cram ML: Generalized ML Learning
model_predict | Cram Policy: Predict with the Specified Model
model_predict_ml | Cram ML: Predict with the Specified Model
set_model | Cram Policy: Set Model
test_baseline_policy | Validate or Set the Baseline Policy
test_batch | Validate or Generate Batch Assignments
validate_params | Cram Policy: Validate User-Provided Parameters for a Model
validate_params_fnn | Cram Policy: Validate Parameters for Feedforward Neural Networks (FNNs)