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