Rbundler manages a project-specific library for dependency
package installation. By specifying dependencies in a DESCRIPTION file
in a project's root directory, one may install and use dependencies
in a repeatable fashion without requiring manual maintenance.
rbundler creates a project-specific R library in
`PROJECT_ROOT/.Rbundle` (by default) and a project-specific
`R_LIBS_USER` value, set in `PROJECT_ROOT/.Renviron`. It supports
dependency management for R standard "Depends", "Imports",
"Suggests", and "LinkingTo" package dependencies. rbundler also
attempts to validate and install versioned dependencies, such
as ">=", "==", "<=". Note that, due to the way R manages package
installation, differing nested versioned dependencies are not
allowed. For example, if your project depends on packages A (== 1),
and B (== 2), but package A depends on B (== 1), then a nested
dependency violation will cause rbundler to error out.

Rafal Baranowski, Patrick Breheny, Isaac Turner
Implements the Ranking-Based Variable Selection
algorithm for variable selection in high-dimensional data.

Residual Balancing Weights for Marginal Structural Models
Residual balancing is a robust method of constructing weights for
marginal structural models, which can be used to estimate marginal effects of
time-varying treatments and controlled direct/mediator effects in causal mediation
analysis (Zhou and Wodtke 2020 <doi:10.1017/pan.2020.2>). This
package provides two main functions, rbwPanel() and rbwMed(), that produce
residual balancing weights for analyzing time-varying treatments and
causal mediation respectively.

c(
person("Xiang", "Zhou", email = "xiang_zhou@fas.harvard.edu", role = "cre"),
person("Derick da Silva", "Baum", email = "derick_baum@g.harvard.edu", role = "aut"))
Residual balancing is a robust method of constructing weights for
marginal structural models, which can be used to estimate (a) the average treatment effect in
a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation
analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020
<doi:10.1017/pan.2020.2>). This package provides three functions, rbwATE(), rbwMed(), and rbwPanel(),
that produce residual balancing weights for estimating (a), (b), (c), respectively.
Xiang Zhou [cre],
Derick da Silva Baum [aut]