Changes/Bug Fixes in Version 1.3.0

-- tmle function now returns the additive effect of treatment among the treated (ATT) and among the controls (ATC)

Changes/Bug Fixes in Version 1.2.0-5

-- Updated to be compatible with SuperLearner v2.0-21. SL.glm.interaction no longer masked. 

-- Option to specify number of cross-validation folds for SL estimation of Q and g, default remains V = 5.

Changes/Bug Fixes in Version 1.2.0-4

-- Licences change to BSD_3_clause + file LICENSE | GPL-2

Changes/Bug Fixes in Version 1.2.0-3

-- Licences change to BSD | GPL-2

Changes/Bug Fixes in Version 1.2.0-2

-- Added cite to Journal of Statistical Software publication, "tmle: An R Package for  Targeted Maximum Likelihood Estimation", 51(13), 1-35,
http://www.jstatsoft.org/v51/i13/

-- License change to BSD 

Changes/Bug Fixes in Version 1.2.0-1

-- FEV dataset included withing the package

-- Update examples for tmleMSM()

Changes/Bug Fixes in Version 1.2.0

-- New tmleMSM() function for estimating parameters of an arbitry MSM. Type help(tmleMSM) for details.

-- Now works with SuperLearner versions 1.0 through 2.0-6

    -- Default SuperLearner libraries for Q and g are now set to (SL.glm, SL.step, SL.glm.interaction), 
        all of which are included in the base installation of R.  

    -- Specifying SuperLearner screening algorithms within calls to SuperLearner is not supported. 

    -- If SuperLearner fails while estimating Q, the program quits instead of defaulting to main terms
       regression with glm.  But, if a call to SuperLearner fails when estimating g, the program prints a
       warning message, and defaults to estimating g with main terms regression.  This way, initial 
       estimate of Q, will be returned, and can be supplied to the next invocation of the function 
       without having to be be re-calculated.

-- Interaction terms now allowed in user-specified formulas for estimating Q and g 

-- Factors are allowed in covariate matrix W, but will only work correctly when glm is used to estimate 
   Q and g. (Super Learner does not support factors. Conversion to dummy variables is recommended)

