Cram Method for Efficient Simultaneous Learning and Evaluation


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Documentation for package ‘cramR’ version 0.1.0

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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)