Changes in version 0.2.4                        

  - Updated required versions of some dependencies to their most recent
    stable versions, including hal9001 (v0.4.6) and sl3 (v1.4.5).

                        Changes in version 0.2.3                        

  - Added a new named argument cv_strat to est_onestep() and est_tml()
    and to the estimator_args list-argument in medoutcon(), which allows
    for stratified folds to be generated for cross-fitting (by passing
    these to the strata_ids argument of make_folds() from the origami
    package). This is also triggered by an override in est_onestep() and
    est_tml() when the proportion of detected cases is less than 0.1, a
    heuristic for rare outcomes.
  - Increased the default number of folds for cross-fitting from 5
    to 10, setting cv_folds = 10L in named arguments to est_onestep()
    and est_tml() and to the estimator_args list-argument in
    medoutcon().
  - Changed default propensity score truncation bounds specified in
    g_bounds to c(0.005, 0.995) from c(0.01, 0.99) (in v0.22), based on
    sanity checks and manual experimentation.
  - Wrapped instances of sl3_Task() in which outcome_type = "continuous"
    is specified in suppressWarnings() to sink warnings when the outcome
    variable for a given nuisance estimation task fails sl3's check for
    continuous-ness.

                        Changes in version 0.2.2                        

  - Change iterative targeting procedures in est_tml() to use glm2::glm2
    instead of stats::glm to avoid issues with erratic IRLS in the
    latter; see https://journal.r-project.org/archive/2011/RJ-2011-012/
    for details.
  - Changed default propensity score truncation bounds specified in
    g_bounds by an order of magnitude, from c(0.001, 0.999) to
    c(0.01, 0.99), to mitigate potential stability issues.

                        Changes in version 0.2.1                        

  - Fixes bug in weighted TMLEs introduced during prior update to
    est_tml().

                        Changes in version 0.2.0                        

  - Added support for a semiparametric correction for outcome-dependent
    two-phase sampling designs with known or estimated sampling weights.
  - Tightened sanity checks for estimation of natural direct and
    indirect effects by requiring that EIF scores related to
    intermediate confounders uniformly be zero when Z = NULL is
    specified.
  - The above required minor changes to est_tml() so as to avoid
    fluctuation models for the TMLE step from updating the intermediate
    confounder nuisance components (i.e., q_prime_Z_one,
    q_prime_Z_natural) and causing numerical issues that violate the
    above internal checks.

                        Changes in version 0.1.5                        

  - For user clarity, the name of the argument for providing externally
    computed observation-level weights has changed (from ext_weights) to
    svy_weights.
  - Support for the natural direct and indirect effects has been added,
    requiring the addition of the new internal argument effect_type
    across functions for estimation, including cv_eif(), est_onestep(),
    and est_tml(). When Z = NULL is set in medoutcon(), a natural effect
    estimate corresponding to the argument effect is returned instead of
    an interventional effect.
  - The summary() and print() methods have been updated to allow
    handling of natural effects and counterfactual means under arbitrary
    contrasts.

                        Changes in version 0.1.0                        

  - An initial public release of this package, version 0.1.0, which
    includes support for external observation-level weights.