Package: polle 1.5

polle: Policy Learning

Package for evaluating user-specified finite stage policies and learning optimal treatment policies via doubly robust loss functions. Policy learning methods include doubly robust blip/CATE learning and sequential policy tree learning. The package also include methods for optimal subgroup analysis. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.

Authors:Andreas Nordland [aut, cre], Klaus Holst [aut]

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polle.pdf |polle.html
polle/json (API)
NEWS

# Install 'polle' in R:
install.packages('polle', repos = c('https://andreasnordland.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/andreasnordland/polle/issues

On CRAN:

5.58 score 4 stars 6 scripts 271 downloads 50 exports 55 dependencies

Last updated 3 months agofrom:9f83c481e3. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-winNOTENov 02 2024
R-4.5-linuxNOTENov 02 2024
R-4.4-winOKNov 02 2024
R-4.4-macOKNov 02 2024
R-4.3-winOKNov 02 2024
R-4.3-macOKNov 02 2024

Exports:Allconditionalcontrol_blipcontrol_drqlcontrol_earlcontrol_owlcontrol_ptlcontrol_rwlcopy_policy_dataestimatefit_g_functionsg_empirg_glmg_glmnetg_rfg_slg_xgboostget_action_setget_actionsget_g_functionsget_historyget_history_namesget_idget_id_stageget_Kget_nget_policyget_policy_actionsget_policy_functionsget_policy_objectget_q_functionsget_stage_action_setsget_utilityICpartialpolicy_datapolicy_defpolicy_evalpolicy_learnq_glmq_glmnetq_rfq_slq_xgboostsim_multi_stagesim_single_stagesim_single_stage_multi_actionssim_two_stagesim_two_stage_multi_actionssubset_id

Dependencies:BHbitopscaToolsclicodetoolscvAUCdata.tabledfoptimDiceKrigingdigestDynTxRegimeforeachformatRfutile.loggerfutile.optionsfuturefuture.applygamglobalsgplotsgrfgtoolsiteratorskernlabKernSmoothlambda.rlatticelavalistenvlmtestMatrixmetsmodelObjmvtnormnloptrnnlsnumDerivoptimxparallellypolicytreepracmaprogressrR6RcppRcppArmadilloRcppEigenrgenoudROCRsandwichSQUAREMSuperLearnersurvivaltargetedtimeregzoo

Estimating and Evaluating the Optimal Subgroup

Rendered fromoptimal_subgroup.Rmdusingknitr::rmarkdownon Nov 02 2024.

Last update: 2024-07-29
Started: 2024-07-11

policy_data

Rendered frompolicy_data.Rmdusingknitr::rmarkdownon Nov 02 2024.

Last update: 2024-04-23
Started: 2024-04-23

policy_eval

Rendered frompolicy_eval.Rmdusingknitr::rmarkdownon Nov 02 2024.

Last update: 2024-07-26
Started: 2024-04-23

policy_learn

Rendered frompolicy_learn.Rmdusingknitr::rmarkdownon Nov 02 2024.

Last update: 2024-04-23
Started: 2024-04-23

Readme and manuals

Help Manual

Help pageTopics
Conditional Policy Evaluationconditional
Control arguments for doubly robust blip-learningcontrol_blip
Control arguments for doubly robust Q-learningcontrol_drql
Control arguments for Efficient Augmentation and Relaxation Learningcontrol_earl
Control arguments for Outcome Weighted Learningcontrol_owl
Control arguments for Policy Tree Learningcontrol_ptl
Control arguments for Residual Weighted Learningcontrol_rwl
Copy Policy Data Objectcopy_policy_data
Fit g-functionsfit_g_functions
g_model class objectg_empir g_glm g_glmnet g_model g_rf g_sl g_xgboost
Get Action Setget_action_set
Get Actionsget_actions
Get g-functionsget_g_functions
Get history variable namesget_history_names
Get IDsget_id
Get IDs and Stagesget_id_stage
Get Maximal Stagesget_K
Get Number of Observationsget_n
Get Policyget_policy
Get Policy Actionsget_policy_actions
Get Policy Functionsget_policy_functions get_policy_functions.blip get_policy_functions.drql get_policy_functions.ptl get_policy_functions.ql
Get Policy Objectget_policy_object
Get Q-functionsget_q_functions
Get Stage Action Setsget_stage_action_sets
Get the Utilityget_utility
Get History Objectget_history history
Nuisance Functionsnuisance_functions
Trim Number of Stagespartial
Plot policy data for given policiesplot.policy_data
Plot histogram of the influence curve for a 'policy_eval' objectplot.policy_eval
Policy-classpolicy
Create Policy Data Objectpolicy_data print.policy_data summary.policy_data
Define Policypolicy_def
Policy Evaluation+.policy_eval coef.policy_eval estimate.policy_eval IC.policy_eval merge.policy_eval policy_eval print.policy_eval summary.policy_eval vcov.policy_eval
Create Policy Learnerpolicy_learn policy_object print.policy_learn print.policy_object
Predict g-functions and Q-functionspredict.nuisance_functions
q_model class objectq_glm q_glmnet q_model q_rf q_sl q_xgboost
Simulate Multi-Stage Datasim_multi_stage
Simulate Single-Stage Datasim_single_stage
Simulate Single-Stage Multi-Action Datasim_single_stage_multi_actions
Simulate Two-Stage Datasim_two_stage
Simulate Two-Stage Multi-Action Datasim_two_stage_multi_actions
Subset Policy Data on IDsubset_id