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Bandit
|
Bandit: Superclass |
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BasicBernoulliBandit
|
Bandit: BasicBernoulliBandit |
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BasicGaussianBandit
|
Bandit: BasicGaussianBandit |
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ContextualBernoulliBandit
|
Bandit: Naive Contextual Bernouilli Bandit |
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ContextualBinaryBandit
|
Bandit: ContextualBinaryBandit |
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ContextualHybridBandit
|
Bandit: ContextualHybridBandit |
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ContextualLinearBandit
|
Bandit: ContextualLinearBandit |
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ContextualLogitBandit
|
Bandit: ContextualLogitBandit |
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ContextualPrecachingBandit
|
Bandit: ContextualPrecachingBandit |
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ContextualWheelBandit
|
Bandit: ContextualWheelBandit |
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ContinuumBandit
|
Bandit: ContinuumBandit |
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OfflineBootstrappedReplayBandit
|
Bandit: Offline Bootstrapped Replay |
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OfflineDirectMethodBandit
|
Bandit: Offline Direct Methods |
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OfflineDoublyRobustBandit
|
Bandit: Offline Doubly Robust |
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OfflineLookupReplayEvaluatorBandit
|
Bandit: Offline Replay with lookup tables |
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OfflinePropensityWeightingBandit
|
Bandit: Offline Propensity Weighted Replay |
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OfflineReplayEvaluatorBandit
|
Bandit: Offline Replay |
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BootstrapTSPolicy
|
Policy: Thompson sampling with the online bootstrap |
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ContextualEpochGreedyPolicy
|
Policy: A Time and Space Efficient Algorithm for Contextual Linear Bandits |
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ContextualEpsilonGreedyPolicy
|
Policy: ContextualEpsilonGreedyPolicy with unique linear models |
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ContextualLinTSPolicy
|
Policy: Linear Thompson Sampling with unique linear models |
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ContextualLogitBTSPolicy
|
Policy: ContextualLogitBTSPolicy |
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ContextualTSProbitPolicy
|
Policy: ContextualTSProbitPolicy |
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EpsilonFirstPolicy
|
Policy: Epsilon First |
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EpsilonGreedyPolicy
|
Policy: Epsilon Greedy |
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Exp3Policy
|
Policy: Exp3 |
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FixedPolicy
|
Policy: Fixed Arm |
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GittinsBrezziLaiPolicy
|
Policy: Gittins Approximation algorithm for choosing arms in a MAB problem. |
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GradientPolicy
|
Policy: Gradient |
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LifPolicy
|
Policy: Continuum Bandit Policy with Lock-in Feedback |
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LinUCBDisjointOptimizedPolicy
|
Policy: LinUCB with unique linear models |
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LinUCBDisjointPolicy
|
Policy: LinUCB with unique linear models |
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LinUCBGeneralPolicy
|
Policy: LinUCB with unique linear models |
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LinUCBHybridOptimizedPolicy
|
Policy: LinUCB with hybrid linear models |
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LinUCBHybridPolicy
|
Policy: LinUCB with hybrid linear models |
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OraclePolicy
|
Policy: Oracle |
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Policy
|
Policy: Superclass |
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RandomPolicy
|
Policy: Random |
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SoftmaxPolicy
|
Policy: Softmax |
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ThompsonSamplingPolicy
|
Policy: Thompson Sampling |
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UCB1Policy
|
Policy: UCB1 |
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UCB2Policy
|
Policy: UCB2 |
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data_table_factors_to_numeric()
|
Convert all factor columns in data.table to numeric |
|
get_global_seed()
|
Lookup .Random.seed in global environment |
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set_global_seed()
|
Set .Random.seed to a pre-saved value |
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get_arm_context()
|
Return context vector of an arm |
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get_full_context()
|
Get full context matrix over all arms |
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`dec<-`()
|
Decrement |
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`inc<-`()
|
Increment |
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inv()
|
Inverse from Choleski (or QR) Decomposition. |
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sum_of()
|
Sum of list |
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which_max_list()
|
Get maximum value in list |
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which_max_tied()
|
Get maximum value randomly breaking ties |
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sample_one_of()
|
Sample one element from vector or list |
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is_rstudio()
|
Check if in RStudio |
|
set_external()
|
Change Default Graphing Device from RStudio |
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formatted_difftime()
|
Format difftime objects |
|
dinvgamma() pinvgamma() qinvgamma() rinvgamma()
|
The Inverse Gamma Distribution |
|
invlogit()
|
Inverse Logit Function |
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mvrnorm()
|
Simulate from a Multivariate Normal Distribution |
|
one_hot()
|
One Hot Encoding of data.table columns |
|
sherman_morrisson()
|
Sherman-Morrisson inverse |
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var_welford()
|
Welford's variance |
|
prob_winner()
|
Binomial Win Probability |
|
sim_post()
|
Binomial Posterior Simulator |
|
value_remaining()
|
Potential Value Remaining |
|
clipr()
|
Clip vectors |
|
ind()
|
On-the-fly indicator function for use in formulae |