- Development version
- repeated cross-fitting in
cate
function via the new 'rep' argument - First argument to
ml_model
can be a character defining the response-variable (optional) predictor
wrapper, andpredictor_sl
,predictor_glm
, ...
cate
now also returns the expected potential outcomes and influence functions- Bug-fix in the
ml_model$update()
method - The default scoring method for
cv
now only switches to log-score+brier score when the response is a factor. Custom model-scoring function (cv argument modelscore) automatically gets 'weights' appended to the formal-arguments.
alean
: Assumption Lean inference for generalized linear model parametersate
now supports general family argumentcate
now supports parallelization via the future or parallel packageml_model
refactored.ML
new wrapper for various machine learning models.cv
parallelization (future or parallel package)riskreg_cens
cumulative risk, restricted mean survival predictions (censored unbiased regression estimates)
- Conditional average treatment estimator
cate
,crr
- Generic prediction model class
ml_model
- design
- SuperLearner wrapper
SL
- Average Treatment among responders
RATE
- Weighted Naive Bayes classifer with
NB
- Pooled adjacent violator algorithm
pava
- ODE solver
ode_solve
- Calibration
calibration
- Cross-validation
cv
ace
method updated and renamed toate
- Maintenance release.
- Initialization of the new package
targeted
with implementation of augmented inverse probability weighting methods for estimation with missing data and causal inference (aipw
,ace
), and double robust methods for risk regression with binary exposure variables (riskreg
).