v0.3.2

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