v0.6.0

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