RCAL (version 2.0)

Regularized Calibrated Estimation

Description

Regularized calibrated estimation for causal inference and missing-data problems with high-dimensional data, based on Tan (2020a) , Tan (2020b) and Sun and Tan (2020) .

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Version

Install

install.packages('RCAL')

Monthly Downloads

215

Version

2.0

License

GPL (>= 2)

Maintainer

Zhiqiang Tan

Last Published

November 5th, 2020

Functions in RCAL (2.0)

ate.nreg

Model-assisted inference for average treatment effects without regularization

glm.regu.cv

Regularied M-estimation for fitting generalized linear models based on cross validation

ate.regu.cv

Model-assisted inference for average treatment effects based on cross validation

glm.regu.path

Regularied M-estimation for fitting generalized linear models along a regularization path

RCAL-package

RCAL: Regularized calibrated estimation

ate.aipw

Augmented inverse probability weighted estimation of population means

ate.ipw

Inverse probability weighted estimation of average treatment effects

ate.regu.path

Model-assisted inference for average treatment effects along regularization paths

glm.nreg

Non-regularied M-estimation for fitting generalized linear models

mn.aipw

Augmented inverse probability weighted estimation of population means

glm.regu

Regularied M-estimation for fitting generalized linear models with a fixed tuning parameter

simu.iv.data

Simulated instrumental variable data

mn.regu.cv

Model-assisted inference for population means based on cross validation

mn.nreg

Model-assisted inference for population means without regularization

late.nreg

Model-assisted inference for local average treatment effects without regularization

late.aipw

Augmented inverse probability weighted estimation of local average treatment effects

mn.ipw

Inverse probability weighted estimation of population means

simu.data

Simulated data

mn.regu.path

Model-assisted inference for population means along a regularization path

late.regu.cv

Model-assisted inference for local average treatment effects (LATEs) with instrumental variables based on cross validation

late.regu.path

Model-assisted inference for local average treatment effects along regularization paths