ltmle 0.9-5
===========

MAJOR CHANGES

* Variance estimation in the presence of positivity violations and/or rare events has been significantly improved. However, the new variance estimation procedure takes significantly more time to run and is not yet compatible with some options (deterministic.Q.function, non-binary outcomes, gcomp=TRUE, stratify=TRUE, numeric gform). To obtain the influence curve based variance estimates used in prior versions, use IC.variance.only=TRUE.

* Observations weights (sampling weights) are now available.

* Baseline covariates can now be part of working.msm. 

* A change to the implementation of the TMLE updating step reduces the variance of the estimator.

* The weight.msm (TRUE or FALSE) parameter has been removed and replaced by msm.weights, which provides more flexibility in specifying MSM weights. 

* The stratified IPTW for MSMs has been replaced by the standard IPTW for MSMs.

* The usage for obtaining additive treatment effects, relative risk, and odds ratio has changed. 

MINOR CHANGES

* The pooledMSM parameter has been removed. The pooedMSM=TRUE version is always used.

* The mhte.iptw parameter has been removed. The mhte.iptw=TRUE version is always used. This may give different answers to previous versions if you were using the default setting of mhte.iptw=FALSE.

* The naive estimator in ltmle has been removed. One way this can still be obtained is by passing numeric gform such that prob(follow rule)=1 and using the "iptw" estimate.

* The cumulative g without bounding is now returned (cum.g.unbounded) along with the bounded version (cum.g).

* Improved estimate of time to completion.

* Confidence intervals truncated at [0, 1] for treatment specific mean, [-1, 1] for additive treatment effect.

* A bug has been fixed in the output for relative risk and odds ratio: the output that was incorrectly labelled "Estimated Std Err" now reads "Est Std Err log(OR)" or "Est Std Err log(RR)". 

ltmle 0.9.3-1
===========
* Changed DESCRIPTION version dependency to R >= 3.0.0 because of testthat update.


ltmle 0.9.3
===========

* A bug has been fixed where if your A values were not set to NA after death, IPTW point treatment estimates were incorrect and if weight.msm=TRUE, the weights were calculated incorrectly in ltmleMSM.

* A bug has been fixed where if you were using glm and had a censoring node at which all observations were "uncensored" (as a factor), results could be off by a lot.

* A bug has been fixed where if you were using SuperLearner and had censoring nodes you may have seen an error (even though your data was specified correctly):
Error in ConvertCensoringNodesToBinary(data, nodes$C) : 
  in data, all Cnodes should be factors with two levels, 'censored' and 'uncensored' 

* The 'regimens' input parameter in ltmleMSM has been changed to 'regimes' to better match existing literature.


ltmle 0.9.1
===========

MAJOR CHANGES

* A bug has been fixed in the use of deterministic maps. This has required removing the deterministic.acnode.map and deterministic.Q.map parameters and replacing them with deterministic.g.function and deterministic.Q.function. The new arguments are functions rather than lists. Please recalculate any results from version 0.9 that used deterministic maps! Differences in most cases should be minor, but could be significant in some cases. My apologies for the error and for the trouble updating your code. Thanks to Linh Tran for finding the bug.

* survivalOutcome parameter has been added and is required when there are multiple binary Y nodes

* scaling for continuous Y values added

MINOR CHANGES

* fits for g and Q regressions are returned

* added BinaryToCensoring helper function

* default SuperLearner library has changed

* messages are used instead of cat and print

* default formulas are shown if gform or Qform is NULL

* dynamic regimes can be specified using a rule function instead of abar

* formulas are returned

* some improved warning messages and error checking

* minor bug fixes


ltmle 0.9
===========

 * initial release to CRAN