method = "glm")
method = "gbm")method = "cbps")method = "npcbps")method = "ebal")method = "ipt")method = "ebcw")method = "optweight")method = "super")method = "bart")method = "energy")WeightIt is a wrapper for several other packages that
aid in estimating balancing weights. In many ways, this is
WeigthIt’s strength, because it is easy to try out several
weighting methods without having to learn a completely new syntax for
each one. One weakness of this is that when one of these packages is not
available (e.g., on CRAN), the method that relies on that package cannot
be used.
This document explains how to install each package
WeightIt uses. You do not need to install every single one;
you only need the one you want to use. For example, the
miseam package provides support for logistic regression
with missing data, but if you have no missing data or you don’t want to
use the approach implemented in miseam, you don’t need to
install it. WeightIt strongly depends on a few packages,
which are automatically installed along with WeightIt, so
you don’t need to worry about installing them separately; these are not
listed here.
Below we note each method (by name and by the input to the
method argument of weightit()) and how to
install the required packages either from CRAN or otherwise when the
CRAN version is not available. In many cases, this involves installing
the package from the author’s GitHub repository, which requires either
the remotes or devtools package, which both
contain the function install_github().
method = "glm")Several options are available for estimating propensity score weights
using GLMs depending on the treatment type and other features of the
desired model. For binary treatments, weightit() uses
stats::glm() by default, and for continuous treatments,
weightit() uses stats::lm() by default, so no
additional packages are required. For multi-category treatments with
multi.method = "weightit", the default,
weightit() uses internal code.
missing = "saem"When missing data is present and missing = "saem" is
supplied, the misaem package is required. To install
misaem from CRAN, run
If misaem is not on CRAN, or if you want to install the
development version from source, you can do so from the developer’s GitHub repo using the
following code:
link = "br.logit"For binary and multi-category treatments, when link is
supplied as "br.logit" or another link beginning with
"br.", the brglm2 package is required. To
install brglm2 from CRAN, run
If brglm2 is not on CRAN, or if you want to install the
development version from source, you can do so from the developer,
Ioannis Kosmidis’s, GitHub
repo using the following code:
brglm2 requires compilation, which means you may need
additional software installed on your computer to install it from
source.
multi.method = "mclogit"For multi-category treatments, when
multi.method = "mclogit", the mclogit package
is required for multinomial logistic regression. To install
mclogit from CRAN, run
If mclogit is not on CRAN, or if you want to install the
development version from source, you can do so from the developer,
Martin Elff’s, GitHub
repo using the following code:
multi.method = "mnp"For multi-category treatments, when
multi.method = "mnp", the MNP package is
required for Bayesian multinomial probit regression. To install
MNP from CRAN, run
If MNP is not on CRAN, or if you want to install the
development version from source, you can do so from the developer,
Kosuke Imai’s, GitHub
repo using the following code:
MNP requires compilation, which means you may need
additional software installed on your computer to install it from
source.
method = "gbm")WeightIt uses the R package gbm to estimate
propensity score weights using GBM. It does not rely on the
twang package at all. To install gbm from
CRAN, run
If gbm is not on CRAN, or if you want to install the
development version from source, you can do so from the developer’s GitHub repo using the
following code:
gbm requires compilation, which means you may need
additional software installed on your computer to install it from
source.
method = "cbps")For method = "cbps", WeightIt uses code
written for WeightIt, so no additional packages need to be
installed to use CBPS.
method = "npcbps")For method = "npcbps", WeightIt uses the R
package CBPS to perform nonparametric covariate balancing
propensity score weighting. To install CBPS from CRAN,
run
If CBPS is not on CRAN, or if you want to install the
development version from source, you can do so from the developer,
Kosuke Imai’s, GitHub
repo using the following code:
method = "ebal")WeightIt uses code written for WeightIt, so
no additional packages need to be installed to use entropy
balancing.
method = "ipt")WeightIt uses the R package rootSolve to
perform the root finding required for inverse probability tilting. To
install rootSolve from CRAN, run
method = "ebcw")Empirical balancing calibration weighting is no longer available with
WeightIt. Use entropy balancing, which in most cases is
mathematically identical.
method = "optweight")WeightIt uses the R package optweight to
perform optimization-based weighting. To install optweight
from CRAN, run
If optweight is not on CRAN, or if you want to install
the development version from source, you can do so from the developer,
Noah Greifer’s (my), GitHub repo using the
following code:
optweight depends on the osqp package,
which requires compilation, which means you may need additional software
installed on your computer to install it from source.
method = "super")WeightIt uses the R package SuperLearner to
estimate propensity score weights using SuperLearner. To install
SuperLearner from CRAN, run
If SuperLearner is not on CRAN, or if you want to
install the development version from source, you can do so from the
developer, Eric Polley’s, GitHub repo using
the following code:
SuperLearner itself is a wrapper for many other
packages. The whole point of using SuperLearner is to include many
different machine learning algorithms to combine them into a
well-fitting stacked model. These algorithms exist in many different R
packages, which each need to be installed to use them. See the Suggested
packages on the SuperLearner CRAN page to
see which packages might be used with SuperLearner.
There are additional functions for use with SuperLearner
in the SuperLearnerExtra repository. To
read these into your R session to be used with
method = "super", use source() on the raw text
file URL. For example, to read in the code for SL.dbarts,
run
method = "bart")WeightIt uses the R package dbarts to
estimate propensity score weights using BART. To install
dbarts from CRAN, run
If dbarts is not on CRAN, or if you want to install the
development version from source, you can do so from the developer,
Vincent Dorie’s, GitHub
repo using the following code:
dbarts requires compilation, which means you may need
additional software installed on your computer to install it from
source.
method = "energy")WeightIt uses the R package osqp to perform
the optimization required for energy balancing. To install
osqp from CRAN, run
If osqp is not on CRAN, or if you want to install the
development version from source, you can do so from the developer’s site
using the instructions given here, though it is a
bit more involved than other installations from source.