Merge pull request #30 from tlverse/catchup

Author: nhejazi

.travis.yml

@@ -5,7 +5,6 @@ branches:
 env:
   global:
   - RGL_USE_NULL=TRUE
-  - secure: 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
 
 language: r
 sudo: required
@@ -19,9 +18,6 @@ r:
   - release
   - devel
 
-before_install:
-  - Rscript -e 'update.packages(ask = FALSE)'
-
 r_packages:
   - devtools
   - sessioninfo
@@ -30,13 +26,13 @@ r_packages:
   - delayed
   - origami
   - hal9001
+  - haldensify
 
 r_github_packages:
   - r-lib/covr
   - r-lib/sessioninfo
   - tlverse/sl3
   - tlverse/tmle3
-  - nhejazi/haldensify
   - nhejazi/txshift
 
 after_success:

CONTRIBUTING.md

@@ -0,0 +1,104 @@
+# Contributing to `tmle3shift` development
+
+We, the authors of the `tmle3shift` R package, use the same guide as is used for
+contributing to the development of the popular `ggplot2` R package. This
+document is simply a formal re-statement of that fact.
+
+The goal of this guide is to help you get up and contributing to `tmle3shift` as
+quickly as possible. The guide is divided into two main pieces:
+
+* Filing a bug report or feature request in an issue.
+* Suggesting a change via a pull request.
+
+## Issues
+
+When filing an issue, the most important thing is to include a minimal
+reproducible example so that we can quickly verify the problem, and then figure
+out how to fix it. There are three things you need to include to make your
+example reproducible: required packages, data, code.
+
+1.  **Packages** should be loaded at the top of the script, so it's easy to
+    see which ones the example needs.
+
+2.  The easiest way to include **data** is to use `dput()` to generate the R
+    code to recreate it. For example, to recreate the `mtcars` dataset in R,
+    I'd perform the following steps:
+
+       1. Run `dput(mtcars)` in R
+       2. Copy the output
+       3. In my reproducible script, type `mtcars <- ` then paste.
+
+    But even better is if you can create a `data.frame()` with just a handful
+    of rows and columns that still illustrates the problem.
+
+3.  Spend a little bit of time ensuring that your **code** is easy for others to
+    read:
+
+    * make sure you've used spaces and your variable names are concise, but
+      informative
+
+    * use comments to indicate where your problem lies
+
+    * do your best to remove everything that is not related to the problem.
+     The shorter your code is, the easier it is to understand.
+
+You can check you have actually made a reproducible example by starting up a
+fresh R session and pasting your script in.
+
+(Unless you've been specifically asked for it, please don't include the output
+of `sessionInfo()`.)
+
+## Pull requests
+
+To contribute a change to `tmle3shift`, you follow these steps:
+
+1. Create a branch in git and make your changes.
+2. Push branch to github and issue pull request (PR).
+3. Discuss the pull request.
+4. Iterate until either we accept the PR or decide that it's not a good fit for
+   `tmle3shift`.
+
+Each of these steps are described in more detail below. This might feel
+overwhelming the first time you get set up, but it gets easier with practice.
+
+If you're not familiar with git or GitHub, please start by reading
+<http://r-pkgs.had.co.nz/git.html>
+
+Pull requests will be evaluated against the a checklist:
+
+1.  __Motivation__. Your pull request should clearly and concisely motivates the
+   need for change. Plesae describe the problem your PR addresses and show
+   how your pull request solves it as concisely as possible.
+
+   Also include this motivation in `NEWS` so that when a new release of
+   ggplot2 comes out it's easy for users to see what's changed. Add your
+   item at the top of the file and use markdown for formatting. The
+   news item should end with `(@yourGithubUsername, #the_issue_number)`.
+
+2.  __Only related changes__. Before you submit your pull request, please
+    check to make sure that you haven't accidentally included any unrelated
+    changes. These make it harder to see exactly what's changed, and to
+    evaluate any unexpected side effects.
+
+    Each PR corresponds to a git branch, so if you expect to submit
+    multiple changes make sure to create multiple branches. If you have
+    multiple changes that depend on each other, start with the first one
+    and don't submit any others until the first one has been processed.
+
+3.  __Use `tmle3shift` coding style__. Please follow the
+    [official ggplot2 style](http://adv-r.had.co.nz/Style.html). Maintaing
+    a consistent style across the whole code base makes it much easier to
+    jump into the code. If you're modifying existing ggplot2 code that
+    doesn't follow the style guide, a separate pull request to fix the
+    style would be greatly appreciated.
+
+4.  If you're adding new parameters or a new function, you'll also need
+    to document them with [roxygen](https://github.com/klutometis/roxygen).
+    Make sure to re-run `devtools::document()` on the code before submitting.
+
+This seems like a lot of work but don't worry if your pull request isn't
+perfect. It's a learning process. A pull request is a process, and unless
+you've submitted a few in the past it's unlikely that your pull request will be
+accepted as is. Please don't submit pull requests that change existing
+behaviour. Instead, think about how you can add a new feature in a minimally
+invasive way.

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: tmle3shift
 Title: Targeted Learning of the Causal Effects of Stochastic Interventions
-Version: 0.1.9
+Version: 0.2.0
 Authors@R: c(
     person("Nima", "Hejazi", email = "nh@nimahejazi.org",
            role = c("aut", "cre", "cph"),
@@ -18,10 +18,10 @@ Description: Targeted maximum likelihood estimation (TMLE) of population-level
     variable importance analyses. Tools are provided for TML estimation of the
     counterfactual mean under a stochastic intervention characterized as a
     modified treatment policy, such as treatment policies that shift the natural
-    value of the exposure. The methodology implemented was first
-    described by I. Díaz and M.J. van der Laan (2013;
-    <doi:10.1111/j.1541-0420.2011.01685.x>) and expanded upon in I. Díaz and
-    M.J. van der Laan (2018).
+    value of the exposure. The causal parameter and estimation were described in
+    Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x> and an
+    improved estimation approach was given by Díaz and van der Laan (2018)
+    <doi:10.1007/978-3-319-65304-4_14>.
 Depends:
     R (>= 3.4.0)
 License: GPL-3
@@ -31,32 +31,30 @@ Imports:
     methods,
     data.table,
     assertthat,
-    tmle3
+    tmle3 (>= 0.2.0)
 Suggests:
     testthat,
     knitr,
     rmarkdown,
     covr,
     stats,
     ggplot2,
-    sl3 (>= 1.3.7),
-    txshift (>= 0.3.1),
+    sl3 (>= 1.4.2),
+    txshift (>= 0.3.5),
     haldensify (>= 0.0.5),
     hal9001,
     xgboost,
     ranger,
     Rsolnp,
-    nnls,
+    nnls
 Remotes:
     github::tlverse/sl3,
-    github::tlverse/tmle3@devel,
-    github::nhejazi/haldensify,
-    github::nhejazi/txshift
+    github::tlverse/tmle3
 URL: https://tlverse.org/tmle3shift
 BugReports: https://github.com/tlverse/tmle3shift/issues
 Encoding: UTF-8
 LazyData: true
 LazyLoad: yes
 VignetteBuilder: knitr
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.1
 Roxygen: list(markdown = TRUE, r6 = FALSE)

README.Rmd

@@ -21,6 +21,7 @@ knitr::opts_chunk$set(
 [![Coverage Status](https://img.shields.io/codecov/c/github/tlverse/tmle3shift/master.svg)](https://codecov.io/github/tlverse/tmle3shift?branch=master)
 [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
 [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](http://www.gnu.org/licenses/gpl-3.0)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4603372.svg)](https://doi.org/10.5281/zenodo.4603372)
 
 > Targeted Learning of the Causal Effects of Stochastic Interventions
 
@@ -75,6 +76,33 @@ issue](https://github.com/tlverse/tmle3shift/issues).
 
 ---
 
+## Contributions
+
+Contributions are very welcome. Interested contributors should consult our
+[contribution
+guidelines](https://github.com/tlverse/tmle3shift/blob/master/CONTRIBUTING.md)
+prior to submitting a pull request.
+
+---
+
+## Citation
+
+After using the `tmle3shift` R package, please cite the following:
+
+        @software{hejazi2021tmle3shift-rpkg,
+          author = {Hejazi, Nima S and Coyle, Jeremy R and {van der Laan}, Mark
+            J},
+          title = {{tmle3shift}: {Targeted Learning} of the Causal Effects of
+            Stochastic Interventions},
+          year = {2021},
+          howpublished = {\url{https://github.com/tlverse/tmle3shift}},
+          note = {{R} package version 0.2.0},
+          url = {https://doi.org/10.5281/zenodo.4603372},
+          doi = {10.5281/zenodo.4603372}
+        }
+
+---
+
 ## Related
 
 * [R/`txshift`](https://github.com/nhejazi/txshift) - An R package providing an

README.md

@@ -14,6 +14,7 @@ state and is being actively
 developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
 [![License: GPL
 v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](http://www.gnu.org/licenses/gpl-3.0)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4603372.svg)](https://doi.org/10.5281/zenodo.4603372)
 
 > Targeted Learning of the Causal Effects of Stochastic Interventions
 
@@ -34,11 +35,12 @@ builds upon the core `tlverse` grammar introduced by `tmle3`, a general
 framework that supports the implementation of a range of TMLE parameters
 through a unified interface. For a detailed description of the target
 parameter, TML estimator, and algorithm implemented in `tmle3shift`, the
-interested reader is invited to consult Díaz and van der Laan (2012) and
-Díaz and van der Laan (2018). For a general discussion of the framework
-of targeted minimum loss-based estimation and the role this methodology
-plays in statistical and causal inference, the canonical references are
-van der Laan and Rose (2011) and van der Laan and Rose (2018).
+interested reader is invited to consult Dı́az and van der Laan (2012)
+and Dı́az and van der Laan (2018). For a general discussion of the
+framework of targeted minimum loss-based estimation and the role this
+methodology plays in statistical and causal inference, the canonical
+references are van der Laan and Rose (2011) and van der Laan and Rose
+(2018).
 
 Building on the original work surrounding the TML estimator for the
 aforementioned target parameter, `tmle3shift` additionally implements a
@@ -70,6 +72,35 @@ If you encounter any bugs or have any specific feature requests, please
 
 -----
 
+## Contributions
+
+Contributions are very welcome. Interested contributors should consult
+our [contribution
+guidelines](https://github.com/tlverse/tmle3shift/blob/master/CONTRIBUTING.md)
+prior to submitting a pull request.
+
+-----
+
+## Citation
+
+After using the `tmle3shift` R package, please cite the following:
+
+``` 
+    @software{hejazi2021tmle3shift-rpkg,
+      author = {Hejazi, Nima S and Coyle, Jeremy R and {van der Laan}, Mark
+        J},
+      title = {{tmle3shift}: {Targeted Learning} of the Causal Effects of
+        Stochastic Interventions},
+      year = {2021},
+      howpublished = {\url{https://github.com/tlverse/tmle3shift}},
+      note = {{R} package version 0.2.0},
+      url = {https://doi.org/10.5281/zenodo.4603372},
+      doi = {10.5281/zenodo.4603372}
+    }
+```
+
+-----
+
 ## Related
 
   - [R/`txshift`](https://github.com/nhejazi/txshift) - An R package
@@ -100,9 +131,9 @@ See file `LICENSE` for details.
 
 <div id="ref-diaz2012population">
 
-Díaz, Iván, and Mark J van der Laan. 2012. “Population Intervention
-Causal Effects Based on Stochastic Interventions.” *Biometrics* 68 (2).
-Wiley Online Library: 541–49.
+Dı́az, Iván, and Mark J van der Laan. 2012. “Population Intervention
+Causal Effects Based on Stochastic Interventions.” *Biometrics* 68 (2):
+541–49.
 
 </div>
 

appveyor.yml

@@ -25,8 +25,6 @@ environment:
     PKGTYPE: both
     R_REMOTES_STANDALONE: true
     R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
-    GITHUB_PAT:
-      secure: Nu6c5CmcVdYHDUVGej5ZIXGKOXVPBwqUwjrLV8d6UlbUaAtYuUhgpNuv9oLztAQQ
 
   matrix:
     - R_VERSION: release
@@ -36,9 +34,8 @@ build_script:
   - echo Current directory=%CD%
   - travis-tool.sh install_r base64
   - travis-tool.sh install_github r-lib/sessioninfo r-lib/covr
-  - travis-tool.sh install_github tlverse/delayed tlverse/origami
-  - travis-tool.sh install_github tlverse/hal9001 tlverse/sl3 tlverse/tmle3
-  - travis-tool.sh install_github nhejazi/haldensify nhejazi/txshift
+  - travis-tool.sh install_github tlverse/sl3 tlverse/tmle3
+  - travis-tool.sh install_github nhejazi/txshift
   - travis-tool.sh install_deps
 
 test_script: