Zepid

Warning for upcoming change for `IPTW` in v0.8.0. To better align with other causal estimators, `IPTW` will no longer
only return a vector of weights. Behind the scenes, `IPTW` will be able to estimate the marginal structural model
and provide the results directly in v0.8.0. `IPTW` will still allow access to the `Weight` column. Other tweaks are
coming, such as `IPTW` estimators built for different data types. For example, `SurvivalIPTW` for survival data (like
`SurvivalGFormula`).

Stochastic treatments can be estimated with the new `StochasticIPTW` class. This class is different from `IPTW` in that
it provides the estimated mean of the outcome given the treatment plan. For comparisons, multiple versions of treatment
plans need to be specified, calculated, then compared. For confidence intervals, a bootstrap procedure should be used

G-estimation of structural nested models (for a single time point) are now available through `GEstimationSNM`. Psi
parameters can be calculated using a closed form solution or via a `scipy` optimization procedure

Survival analysis g-formula is now implemented with `SurvivalGFormula`. This g-formula implementation is for
time-to-event data, where the treatment/exposure is determined at baseline. This does not allow for time-varying
exposures. For time-varying exposures, `MonteCarloGFormula` or `IterativeCondGFormula` should be used instead

`summary()` functions have been updated to provide more information regarding the model

Added a calculator function for Rubin's Rule to merge multiple imputation results. Input is a list of point estimates
and a list of variance estimates for `rubins_rules()`. This function returns a summary point estimate and summary
variance

Weighted models are switched from `GEE` to `GLM` when possible. GEE takes extra computation time. GLM provides the
correct point estimates, but wrong variance. Since I don't need the variance to be correct from most models, I switched
to GLM. This improves the speed of fitting weighted models. Especially important for bootstrapping procedures

Aligned `exposure` and `outcome` references with the causal functions. All classes now use the same labels for the
exposure and the outcome column labels.

Updated ReadTheDocs website

AIPTW`` now supports continuous outcomes (normal or Poisson). Format is the same as `TMLE`.

`AIPTW` and `IPTW` now include the optional argument `weights`

Fixed `TMLE` attribute for average treatment effect confidence intervals, from `average_treatment_effect_ic` to
`average_treatment_effect_ci`

Fixed issue in `IPTW` assumption calculations. Depending on when `positivity()` was called, it changed the results of
`plot_love()`.

MonteCarloGFormula` now includes a separate `censoring_model()` function for informative censoring.
Additionally, I added a low memory option to reduce the memory burden during the Monte-Carlo procedure

``IterativeCondGFormula`` has been refactored to accept only data in a wide format. This allows for me to handle more
complex treatment assignments and specify models correctly. Additional tests have been added comparing to R's `ltmle`

There is a new branch in `zepid.causal`. This is the `generalize` branch. It contains various tools for generalizing
or transporting estimates from a biased sample to the target population of interest. Options available are
inverse probability of sampling weights for generalizability (`IPSW`), inverse odds of sampling weights for
transportability (`IPSW`), the g-transport formula (`GTransportFormula`), and doubly-robust augmented inverse
probability of sampling weights (`AIPSW`)

`RiskDifference` now calculates the Frechet probability bounds

``TMLE`` now allows for specified bounds on the Q-model predictions. Additionally, avoids error when predicted
continuous values are outside the bounded values.

``AIPTW`` now has confidence intervals for the risk difference based on influence curves

``spline`` now uses `numpy.percentile` to allow for older versions of NumPy. Additionally, new function
`create_spline_transform` returns a general function for splines, which can be used within other functions

Lots of documentation updates for all functions. Additionally, `summary()` functions are starting to be updated.
Currently, only stylistic changes

While conducting further testing, I found an error in `AIPTW`. I have since corrected it and added additional tests
to `tests/test_doublyrobust.py`. Please rerun any analyses ran that used `AIPTW`

Added a fix to ``TMLE`` for machine learning libraries and missing outcome data