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ateBootstrap
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Compute bootstrap confidence intervals on the ATE parameter |
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ateTMLE
|
Compute TMLE on the ATE parameter |
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ateTuneHyperparam
|
Use plateau method to choose the L1 penalty of HAL (ATE) |
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avgDensityBootstrap
|
Compute bootstrap confidence intervals on the average squared density parameter |
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avgDensityTMLE
|
onestep TMLE of average density parameter |
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avgDensityTuneHyperparam
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Use plateau method to choose the L1 penalty of HAL (average squared density) |
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basic_fixed_HAL()
|
(Experimental) Super Learner wrapper for fixed HAL |
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blipVarContinuousYTuneHyperparam
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Use plateau method to choose the L1 penalty of HAL (blip variance) |
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blipVarianceBootstrap
|
Bootstrap confidence intervals for the blip variance parameter (binary Y) |
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blipVarianceBootstrapContinuousY
|
Bootstrap confidence intervals for the blip variance parameter (continuous Y) |
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blipVarianceTMLE
|
Compute TMLE on the variance of CATE (binary Y) |
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blipVarianceTMLEContinuousY
|
Compute TMLE on the variance of CATE (continuous Y) |
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comprehensiveBootstrap
|
Run `generalBootstrap` twice (regular + second-order bootstrap) |
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cvDensityHAL
|
Fit a 1-d density using HAL regression; automatic tuning of L1 penalty |
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densityHAL
|
Fit a 1-d density using HAL regression |
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empiricalDensity
|
Store a 1-dimensional density function |
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fit_fixed_HAL()
|
fitting fixed_HAL. outputs an object of the fit
use the old basis
use the old lambda
OPTIONAL: if the old object is squashed, only use the non-zero basis |
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generalBootstrap
|
Abstract class of bootstrap |
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grabPlateau
|
Grab a plateau of a function y = f(x) |
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longiData
|
Convert univariate series to longitudinal format |
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tuneHyperparam
|
Use plateau method to choose the L1 penalty of HAL (abstract class) |