Zepid

Support for Python 3.7 has been added

``AIPW`` has been removed. It has been replaced with ``AIPTW``

``TMLE`` now supports continuous outcomes (normal or Poisson) and allows for missing outcome data to be missing at
random. This matches more closely to the functionality to R's `tmle`

``IPMW`` allows for monotone missing data.

``MonteCarloRR`` for probabilistic bias analysis allows for random error to be incorporated

``TimeVaryGFormula`` is separated into ``MonteCarloGFormula`` and ``IterativeCondGFormula``. This change is for
maintenance of the estimators and to avoid confusion since they are sufficiently distinct. Originally, I was unaware of
the iterative conditional estimator, which is why the original name was based on time-varying g-formula. While they are
related, it is more confusing to wrap them both in the same class. ``TimeVaryGFormula`` will stick around to v0.6.0.
Going forward it will be cut. It will not be updated going forward

L'Abbe plots are now supported. These plots are useful for visualizing additive and multiplicative interactions for
risk estimates. These are valid for either associations or causal effects.

``IPTW.plot_love`` now displays the legend. I have previously not included this in the function (I should have)

``TMLE`` refactored to estimate machine learners via an outside function. Also converts all pd.Series to np.array to
avoid some unhappiness with sklearn / supylearner models

**MAJOR CHANGES**:

``TMLE`` defaults to calculate all possible measures (risk difference, risk ratio, odds ratio) rather than individual
ones

``TimeFixedGFormula`` allows stochastic interventions for binary exposures. For a stochastic intervention, *p* percent
of the population is randomly treated. This process is repeated *n* times and mean is the marginal outcome. Stochastic
interventions may better align with real-world interventions (often you intervention will **not** be able to treat
*everyone*). Additionally, conditional probabilities are implemented for stochastic interventions. For example, those
with *C=1* might be treated randomly at *p*, while those with *C=0* are treated at *q*.

``IPTW.standard_mean_difference`` and ``IPTW.plot_love`` both support categorical variables. Categorical variables must
be modeled with ``patsy``'s ``C(.)`` keyword. Otherwise, the dummy variables will be treated as binary variables

**MINOR CHANGES**:

Added case-control example data set. ``load_case_control_data()``

Changed rounding in Table 1 generator

**MAJOR CHANGES**:

``TimeFixedGFormula`` supports Poisson and normal distributed continuous outcomes

``IPTW``'s ``plot_kde`` and ``plot_boxplot`` can plot either the probabilities of treatment or the log-odds

``IPTW`` allows for sklearn or supylearner to generate predicted probabilities. Similar to ``TMLE``

``IPTW`` now allows for Love plot to be generated. These plots are valuable for assessing covariate balance via absolute
standardized mean differences. See Austin & Stuart 2015 for an example. In its current state ``IPTW.plot_love`` is
"dumb", in the sense that it plots all variables in the model. If you have a quadratic term in the model for a
continuous variable, it plots both the linear and quadratic terms. However, it is my understanding that you only need
to look at the linear term. These plots are not quite for publication, rather they are useful for quick diagnostics

``IPTW.standardized_mean_differences`` now calculates for all variables automatically. This is used in the background
for the ``plot_love``. For making publication-quality Love plots, I would recommend using the returned DataFrame from
this function and creating a plot manually. *Note* it only returns standardized differeneces, not absolute standardized
differences. Love plots use the standardized differences.
*WARNING:* standardized differences **only** supports binary or continuous variables. Categorical variables are NOT
supported. This will be fixed in v0.4.2 update

**MINOR CHANGES**:

Website updated to reflect above changes and correcting errors I had missed on last check

**MAJOR CHANGES**:

``TMLE`` has been modified to estimate the custom user models now, rather than take the input. This better corresponds
to R's tmle (however, R does the entire process in the background. You must specify for this implementation). The reason
for this major change is that ``LTMLE`` requires an iterative process. The iterative process requires required fitting
based on predicted values. Therefore, for ``LTMLE`` an unfitted model must be input and repeatedly fit. ``TMLE`` matches
this process.

``TimeVaryGFormula`` supports both Monte Carlo estimation and Sequential Regression (interative conditionals) this
added approach reduces some concern over model misspecification. It is also the process used by LTMLE to estimate
effects of interventions. Online documentation has been updated to show how the sequential regression is estimated and
demonstrates how to calculated cumulative probabilities for multiple time points

All calculator functions now return named tuples. The returned tuples can be index via ``returned[0]`` or
``returned.point_estimate``

Documentation has been overhauled for all functions and at ReadTheDocs

Tests have been added for all currently available functions.

Travis CI has been integrated for continuous testing

**MINOR CHANGES**:

``AIPW`` drops missing data. Similar to ``TMLE``

``IPTW`` calculation of standardized differences is now the ``stabilized_difference`` function instead of the previously
used ``StandardDifference``. This change is to follow PEP guidelines

The ``psi`` argument has been replaced with ``measure`` in ``TMLE``. The print out still refers to psi. This update is
to help new users better understand what the argument is for

Better errors for ``IPTW`` and ``IPMW``, when a unstabilized weight is requested but a numerator for the model is
specified

**MAJOR CHANGES**:

``TMLE`` now allows estimation of risk ratios and odds ratios. Estimation procedure is based on ``tmle.R``

``TMLE`` variance formula has been modified to match ``tmle.R`` rather than other resources. This is beneficial for
future implementation of missing data adjustment. Also would allow for mediation analysis with TMLE (not a priority
for me at this time).

``TMLE`` now includes an option to place bounds on predicted probabilities using the ``bound`` option. Default is to use
all predicted probabilities. Either symmetrical or asymmetrical truncation can be specified.

``TimeFixedGFormula`` now allows weighted data as an input. For example, IPMW can be integrated into the time-fixed
g-formula estimation. Estimation for weighted data uses statsmodels GEE. As a result of the difference between GLM
and GEE, the check of the number of dropped data was removed.

``TimeVaryGFormula`` now allows weighted data as an input. For example, Sampling weights can be integrated into the
time-fixed g-formula estimation. Estimation for weighted data uses statsmodels GEE.

**MINOR CHANGES**:

Added Sciatica Trial data set. Mertens, BJA, Jacobs, WCH, Brand, R, and Peul, WC. Assessment of patient-specific
surgery effect based on weighted estimation and propensity scoring in the re-analysis of the Sciatica Trial. PLOS
One 2014. Future plan is to replicate this analysis if possible.

Added data from Freireich EJ et al., "The Effect of 6-Mercaptopurine on the Duration of Steriod-induced
Remissions in Acute Leukemia: A Model for Evaluation of Other Potentially Useful Therapy" *Blood* 1963

``TMLE`` now allows general sklearn algorithms. Fixed issue where ``predict_proba()`` is used to generate probabilities
within ``sklearn`` rather than ``predict``. Looking at this, I am probably going to clean up the logic behind this and
the rest of ``custom_model`` functionality in the future

``AIPW`` object now contains ``risk_difference`` and ``risk_ratio`` to match ``RiskRatio`` and ``RiskDifference``
classes