Changes in version 1.6.4                        

  - new citation, doi:10.18637/jss.v116.i04, Journal of Statistical
    Software

                 Changes in version 1.6.3 (2026-05-05)                  

  - policy_def() gains natural_action and stage_number arguments
      - bug fix: c_cox() now passes time, time2 and event to mets::phreg

                 Changes in version 1.6.1 (2025-12-01)                  

  - policy learning and evaluation under right-censoring/missing outcome

                 Changes in version 1.6.0 (2025-10-30)                  

  - online estimation/sequential validation for (subgroup) policy
    evaluation
      - improved functionality for subgroup analysis (subgroup average
        treatment effect)

                        Changes in version 1.5.1                        

  - documentation and print method for policy_eval() improved
      - multiple thresholds for policy_learn()
      - bug fixes: get_policy_functions(), predict.blip_function(),
        predict.q_glmnet()

                  Changes in version 1.5 (2024-09-06)                   

  - added target argument in policy_eval for estimating the subgroup
    average treatment effect.
      - added threshold argument in policy_learn for learning the
        optimal subgroup.
      - added vignette for learning and evaluating the optimal subgroup.

                  Changes in version 1.4 (2024-04-25)                   

  - vignettes added for policy_data, policy_learn and policy_eval.

                  Changes in version 1.3 (2023-07-06)                   

  - new policy_learn type: "blip". Similar to "drql", but only a single
    model is fitted using the doubly robust score for the blip.
      - q_sl now uses the folds from policy_learn when it is used a
        policy model for "blip" and "drql".
      - g_xgboost and q_xgboost.

                  Changes in version 1.2 (2023-02-07)                   

  - action sets can now vary across stages (stage_action_sets)
      - g_empir() is a new g-model useful for calculating the empirical
        (conditional) probabilities
      - conditional() estimates the group specific policy value
        estimates
      - progressr is now implemented for policy_eval()
      - sim_single_stage(), sim_two_stage(), and sim_multi_stage() are
        new functions for simulating data.

                  Changes in version 1.0 (2022-12-06)                   

  - Package documentation: https://arxiv.org/abs/2212.02335