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polle 1.6.7

- `q_sl()` and `g_sl()` are reinstated, with `SuperLearner (>=2.0-42)`
in `Suggests` rather than `Depends`.

polle 1.6.6

- bug fix: the risk set used by `fit_c_functions()` and
    `predict.c_functions()` now correctly excludes subjects whose
    terminal event has already occurred (events in {0, 2} at stages 1..K
    and {1, 2} at stage K+1).
- bug fix: `policy_eval(type = "dr")$coef_ipw` is now correctly
    computed when some subjects terminate before stage K+1.
- breaking: `policy_eval(type = "ipw")` and `policy_eval(type = "or")`
    are no longer supported. The accompanying inverse-probability-weighted
    and outcome-regression-only estimates remain available on a
    `type = "dr"` fit as `coef_ipw` and `coef_or`.
    Calling `policy_eval()` with `type = "ipw"` or `type = "or"` now
    raises an informative error pointing at the migration path.

polle 1.6.5

- `SuperLearner` was removed from the package dependencies in
  anticipation of its CRAN archival; it has since been reinstated as
  `Suggests`, see the 1.6.6 entry above. `q_sl()` and `g_sl()` were
  temporarily unavailable in this release.
- `control_owl()` gains a `solver` argument, defaulting to `"ipop"`.
  `DTRlearn2` (>= 2.1) changed the default weighted-SVM solver of
  `DTRlearn2::owl()` to `"svm"` (via `WeightSVM::wsvm`)
- bug fix: final-outcome and discrete-censoring model fitting to complete after
    warnings, retaining the fitted models and preserving the learner warnings.
- bug fix: set Q_{K+1} = U for early terminal events under missing final outcome

polle 1.6.4

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

polle 1.6.3

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

polle 1.6.1

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

polle 1.6.0

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

polle 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()

polle 1.5

- 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.

polle 1.4

- vignettes added for policy_data, policy_learn and policy_eval.

polle 1.3

- 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.

polle 1.2

- 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.

polle 1.0

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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