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2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: cdgd
Title: Causal Decomposition of Group Disparities
Version: 0.3.3.9000
Version: 0.3.4
Authors@R:
person("Ang", "Yu", , "ang_yu@outlook.com", role = c("aut", "cre", "cph"),
comment = c(website = "<https://ang-yu.github.io/>", ORCID = "0000-0002-1828-0165"))
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# cdgd (development version)

# cdgd 0.3.4

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# Platform

|field |value |
|:--------|:------------------------------------------------------------------------------------------|
|version |R version 4.3.2 (2023-10-31) |
|os |macOS Sonoma 14.2.1 |
|system |aarch64, darwin20 |
|ui |RStudio |
|language |(EN) |
|collate |en_US.UTF-8 |
|ctype |en_US.UTF-8 |
|tz |Asia/Shanghai |
|date |2024-01-06 |
|rstudio |2023.12.0+369 Ocean Storm (desktop) |
|pandoc |3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown) |

# Dependencies

|package |old |new |Δ |
|:-------|:-----|:----------|:--|
|cdgd |0.3.3 |0.3.3.9000 |* |

# Revdeps

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package,old,new,delta
cdgd,0.3.3,0.3.3.9000,*
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## revdepcheck results

We checked 0 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.

* We saw 0 new problems
* We failed to check 0 packages

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*Wow, no problems at all. :)*
32 changes: 32 additions & 0 deletions revdep/library.noindex/cdgd/new/cdgd/DESCRIPTION
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Package: cdgd
Title: Causal Decomposition of Group Disparities
Version: 0.3.3.9000
Authors@R:
person("Ang", "Yu", , "ang_yu@outlook.com", role = c("aut", "cre", "cph"),
comment = c(website = "<https://ang-yu.github.io/>", ORCID = "0000-0002-1828-0165"))
Description: The framework of causal decomposition of group disparities developed by
Yu and Elwert (2023)
<arXiv:2306.16591>.
This package implements the decomposition estimators
that are based on efficient influence functions. For the
nuisance functions of the estimators, both parametric and
nonparametric options are provided, as well as manual options in case
the default models are not satisfying.
License: MIT + file LICENSE
URL: https://github.com/ang-yu/cdgd
BugReports: https://github.com/ang-yu/cdgd/issues
Depends: R (>= 4.0.0)
Imports: caret (>= 6.0.0)
Suggests: gbm (>= 2.1.8), nnet (>= 7.3.0), ranger (>= 0.14.1)
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
RemoteType: local
RemoteUrl: /Users/Ang/Desktop/Research/Causal_decomposition/cdgd
NeedsCompilation: no
Packaged: 2024-01-06 13:50:12 UTC; Ang
Author: Ang Yu [aut, cre, cph] (<https://ang-yu.github.io/>,
<https://orcid.org/0000-0002-1828-0165>)
Maintainer: Ang Yu <ang_yu@outlook.com>
Built: R 4.3.2; ; 2024-01-06 13:50:12 UTC; unix
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cdgd0_manual Perform unconditional decomposition with
nuisance functions estimated beforehand
cdgd0_ml Perform unconditional decomposition via machine
learning
cdgd0_pa Perform unconditional decomposition via
parametric models
cdgd1_manual Perform conditional decomposition with nuisance
functions estimated beforehand
cdgd1_ml Perform conditional decomposition via machine
learning
cdgd1_pa Perform conditional decomposition via
parametric models
exp_data Simulated example data
2 changes: 2 additions & 0 deletions revdep/library.noindex/cdgd/new/cdgd/LICENSE
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YEAR: 2023
COPYRIGHT HOLDER: cdgd authors
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# Generated by roxygen2: do not edit by hand

export(cdgd0_manual)
export(cdgd0_ml)
export(cdgd0_pa)
export(cdgd1_manual)
export(cdgd1_ml)
export(cdgd1_pa)
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# cdgd (development version)

# cdgd 0.3.4

* Corrected an error in the example of cdgd1_manual

# cdgd 0.3.3

* Now allows for a binary outcome.

# cdgd 0.3.2

* Revised package description and help files. No change in the actual code.

# cdgd 0.3.1

* Fixed an issue that prevents the ranger option from working when there is only one Q variable.

* Adjusted logical checks for installation of ML packages.

# cdgd 0.3.0

* Made the package compliant with CRAN requirements.
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# File share/R/nspackloader.R
# Part of the R package, https://www.R-project.org
#
# Copyright (C) 1995-2012 The R Core Team
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# A copy of the GNU General Public License is available at
# https://www.r-project.org/Licenses/

local({
info <- loadingNamespaceInfo()
pkg <- info$pkgname
ns <- .getNamespace(as.name(pkg))
if (is.null(ns))
stop("cannot find namespace environment for ", pkg, domain = NA);
dbbase <- file.path(info$libname, pkg, "R", pkg)
lazyLoad(dbbase, ns, filter = function(n) n != ".__NAMESPACE__.")
})
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cdgd0_manual cdgd0_manual
cdgd0_ml cdgd0_ml
cdgd0_pa cdgd0_pa
cdgd1_manual cdgd1_manual
cdgd1_ml cdgd1_ml
cdgd1_pa cdgd1_pa
exp_data exp_data
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<!DOCTYPE html>
<html>
<head><title>R: Causal Decomposition of Group Disparities</title>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
<link rel="stylesheet" type="text/css" href="R.css" />
</head><body><div class="container">
<h1> Causal Decomposition of Group Disparities
<img class="toplogo" src="../../../doc/html/Rlogo.svg" alt="[R logo]" />
</h1>
<hr/>
<div style="text-align: center;">
<a href="../../../doc/html/packages.html"><img class="arrow" src="../../../doc/html/left.jpg" alt="[Up]" /></a>
<a href="../../../doc/html/index.html"><img class="arrow" src="../../../doc/html/up.jpg" alt="[Top]" /></a>
</div><h2>Documentation for package &lsquo;cdgd&rsquo; version 0.3.3.9000</h2>

<ul><li><a href="../DESCRIPTION">DESCRIPTION file</a>.</li>
<li><a href="../NEWS">Package NEWS</a>.</li>
</ul>

<h2>Help Pages</h2>


<table style="width: 100%;">
<tr><td style="width: 25%;"><a href="cdgd0_manual.html">cdgd0_manual</a></td>
<td>Perform unconditional decomposition with nuisance functions estimated beforehand</td></tr>
<tr><td style="width: 25%;"><a href="cdgd0_ml.html">cdgd0_ml</a></td>
<td>Perform unconditional decomposition via machine learning</td></tr>
<tr><td style="width: 25%;"><a href="cdgd0_pa.html">cdgd0_pa</a></td>
<td>Perform unconditional decomposition via parametric models</td></tr>
<tr><td style="width: 25%;"><a href="cdgd1_manual.html">cdgd1_manual</a></td>
<td>Perform conditional decomposition with nuisance functions estimated beforehand</td></tr>
<tr><td style="width: 25%;"><a href="cdgd1_ml.html">cdgd1_ml</a></td>
<td>Perform conditional decomposition via machine learning</td></tr>
<tr><td style="width: 25%;"><a href="cdgd1_pa.html">cdgd1_pa</a></td>
<td>Perform conditional decomposition via parametric models</td></tr>
<tr><td style="width: 25%;"><a href="exp_data.html">exp_data</a></td>
<td>Simulated example data</td></tr>
</table>
</div></body></html>
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@media screen {
.container {
padding-right: 10px;
padding-left: 10px;
margin-right: auto;
margin-left: auto;
max-width: 900px;
}
}

.rimage img { /* from knitr - for examples and demos */
width: 96%;
margin-left: 2%;
}

.katex { font-size: 1.1em; }

code {
color: inherit;
background: inherit;
}

body {
line-height: 1.4;
background: white;
color: black;
}

a:link {
background: white;
color: blue;
}

a:visited {
background: white;
color: rgb(50%, 0%, 50%);
}

h1 {
background: white;
color: rgb(55%, 55%, 55%);
font-family: monospace;
font-size: 1.4em; /* x-large; */
text-align: center;
}

h2 {
background: white;
color: rgb(40%, 40%, 40%);
font-family: monospace;
font-size: 1.2em; /* large; */
text-align: center;
}

h3 {
background: white;
color: rgb(40%, 40%, 40%);
font-family: monospace;
font-size: 1.2em; /* large; */
}

h4 {
background: white;
color: rgb(40%, 40%, 40%);
font-family: monospace;
font-style: italic;
font-size: 1.2em; /* large; */
}

h5 {
background: white;
color: rgb(40%, 40%, 40%);
font-family: monospace;
}

h6 {
background: white;
color: rgb(40%, 40%, 40%);
font-family: monospace;
font-style: italic;
}

img.toplogo {
width: 4em;
vertical-align: middle;
}

img.arrow {
width: 30px;
height: 30px;
border: 0;
}

span.acronym {
font-size: small;
}

span.env {
font-family: monospace;
}

span.file {
font-family: monospace;
}

span.option{
font-family: monospace;
}

span.pkg {
font-weight: bold;
}

span.samp{
font-family: monospace;
}

div.vignettes a:hover {
background: rgb(85%, 85%, 85%);
}

tr {
vertical-align: top;
}

span.rlang {
font-family: Courier New, Courier;
color: #666666;
}

31 changes: 31 additions & 0 deletions revdep/library.noindex/cdgd/old/cdgd/DESCRIPTION
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Package: cdgd
Title: Causal Decomposition of Group Disparities
Version: 0.3.3
Authors@R:
person("Ang", "Yu", , "ang_yu@outlook.com", role = c("aut", "cre", "cph"),
comment = c(website = "<https://ang-yu.github.io/>", ORCID = "0000-0002-1828-0165"))
Description: The framework of causal decomposition of group disparities developed by
Yu and Elwert (2023)
<arXiv:2306.16591>.
This package implements the decomposition estimators
that are based on efficient influence functions. For the
nuisance functions of the estimators, both parametric and
nonparametric options are provided, as well as manual options in case
the default models are not satisfying.
License: MIT + file LICENSE
URL: https://github.com/ang-yu/cdgd
BugReports: https://github.com/ang-yu/cdgd/issues
Depends: R (>= 4.0.0)
Imports: caret (>= 6.0.0)
Suggests: gbm (>= 2.1.8), nnet (>= 7.3.0), ranger (>= 0.14.1)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2023-12-09 20:01:24 UTC; Ang
Author: Ang Yu [aut, cre, cph] (<https://ang-yu.github.io/>,
<https://orcid.org/0000-0002-1828-0165>)
Maintainer: Ang Yu <ang_yu@outlook.com>
Repository: CRAN
Date/Publication: 2023-12-09 20:10:02 UTC
Built: R 4.3.1; ; 2023-12-09 23:09:04 UTC; unix
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cdgd0_manual Perform unconditional decomposition with
nuisance functions estimated beforehand
cdgd0_ml Perform unconditional decomposition via machine
learning
cdgd0_pa Perform unconditional decomposition via
parametric models
cdgd1_manual Perform conditional decomposition with nuisance
functions estimated beforehand
cdgd1_ml Perform conditional decomposition via machine
learning
cdgd1_pa Perform conditional decomposition via
parametric models
exp_data Simulated example data
2 changes: 2 additions & 0 deletions revdep/library.noindex/cdgd/old/cdgd/LICENSE
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YEAR: 2023
COPYRIGHT HOLDER: cdgd authors
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