update

.Rbuildignore

@@ -5,3 +5,4 @@
 ^\.github$
 ^LICENSE\.md$
 ^Meta$
+^docs$

.github/.gitignore

@@ -0,0 +1 @@
+*.html

.github/workflows/pkgdown.yaml

@@ -0,0 +1,50 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown.yaml
+
+permissions: read-all
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/[email protected]
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

DESCRIPTION

@@ -1,9 +1,20 @@
 Package: bayesmsm
 Type: Package
-Title: Bayesian Marginal Structural Models
+Title: Fitting Bayesian Marginal Structural Models for Longitudinal observational Data
 Version: 0.1.0
-Author: Kuan Liu, Xiao Yan
-Maintainer: Xiao Yan <[email protected]>
+Date: 2024-10-07
+Authors@R: c(
+    person("Kuan", "Liu", , "[email protected]", role = c("aut", "cre"),
+           comment = "https://Kuan-Liu.com/"),
+    person("Xiao", "Yan", , "[email protected]", role = c("aut", "cre"),
+           comment = "https://www.linkedin.com/in/xiaoyan1596/"),
+    person("Martin", "Urner", ,  role = "aut",
+           comment = "https://criticalcaretoronto.com/our-fellows/dr-martin-urner/"),
+    person("Olli", "Saarela", , "[email protected]", role = "aut",
+           comment = "https://www.dlsph.utoronto.ca/faculty-profile/saarela-olli/"),
+  )
+Maintainer: Kuan Liu <[email protected]>
+Description: Fitting Bayesian marginal structrual models to estimate average treatment effect for drawing causal inference with time-varying treatment assignment and confoudning with extension to handle informative right-censoring. The Bayesian marginal structural models is a semi-parametric approach and features a two-step estimation process. The first step involves Bayesian parametric estimation of the time-varying treatment assignment models and the second step involves non-parametric Bayesian bootstrap to estimate the average treatment effect between two distinct treatment sequences of interest. Based on the Bayesian marginal structural models of Saarela et al (2015) <DOI: 10.1111/biom.12269> and Liu et al (2020) <DOI: 10.1177/0962280219900362>. 
 Depends:
     R (>= 4.2.0)
 Suggests: 
@@ -22,6 +33,5 @@ Imports:
 	  coda (>= 0.19-4)
 NeedsCompilation: no
 Config/testthat/edition: 3
-Description: This package provides tools for estimating causal effects using Bayesian Marginal Structural Models. It includes functions for estimating Bayesian weights using JAGS and for Bayesian non-parametric bootstrap to calculate causal effects.
 URL: https://kuan-liu-lab.github.io/bayesmsm/
 BugReports: https://github.com/Kuan-Liu-Lab/bayesmsm/issues

README.Rmd

@@ -0,0 +1,130 @@
+---
+output: github_document
+editor_options: 
+  chunk_output_type: inline
+---
+
+<!-- README.md is generated from README.Rmd. Please edit this file -->
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  out.width = "100%"
+)
+```
+
+## bayesmsm
+
+<!-- badges: start -->
+<!-- badges: end -->
+
+# Overview
+
+*bayesmsm* is an R package that implements the Bayesian marginal structrual models to estimate average treatment effect for drawing causal inference with time-varying treatment assignment and confoudning with extension to handle informative right-censoring. The Bayesian marginal structural models is a semi-parametric approach and features a two-step estimation process. The first step involves Bayesian parametric estimation of the time-varying treatment assignment models and the second step involves non-parametric Bayesian bootstrap to estimate the average treatment effect between two distinct treatment sequences of interest.
+
+Reference paper on Bayesian marginal structural models:
+
+-  Saarela, O., Stephens, D. A., Moodie, E. E., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288.
+
+-  Liu, K., Saarela, O., Feldman, B. M., & Pullenayegum, E. (2020). Estimation of causal effects with repeatedly measured outcomes in a Bayesian framework. Statistical methods in medical research, 29(9), 2507-2519.
+
+
+# Installation
+
+Install using `devtools` package:
+
+```{r echo=TRUE, eval=FALSE}
+## install.packages(devtools) ## make sure to have devtools installed 
+devtools::install_github("Kuan-Liu-Lab/bayesmsm")
+library(bayesmsm)
+```
+
+
+# Dependency
+
+This package depends on the following R packages:
+
+-  `MCMCpack`
+-  `doParallel`
+-  `foreach`
+-  `parallel`
+-  `R2jags`
+-  `coda`
+
+
+# Quickstart
+
+Here are some examples demonstrating how to use the `bayesmsm` package:
+
+```{r echo=TRUE, eval=FALSE}
+# Load example data
+testdata <- read.csv(system.file("extdata", "continuous_outcome_data.csv", package = "bayesmsm"))
+
+# Calculate Bayesian weights
+weights <- bayesweight(
+  trtmodel.list = list(
+    a_1 ~ w1 + w2 + L1_1 + L2_1,
+    a_2 ~ w1 + w2 + L1_1 + L2_1 + L1_2 + L2_2 + a_1
+  ),
+  data = testdata,
+  n.iter = 250,
+  n.burnin = 150,
+  n.thin = 5,
+  n.chains = 2,
+  seed = 890123,
+  parallel = TRUE
+)
+
+# Perform Bayesian non-parametric bootstrap
+model <- bayesmsm(
+  ymodel = y ~ a_1 + a_2,
+  nvisit = 2,
+  reference = c(rep(0, 2)),
+  comparator = c(rep(1, 2)),
+  family = "gaussian",
+  data = testdata,
+  wmean = weights,
+  nboot = 1000,
+  optim_method = "BFGS",
+  seed = 890123,
+  parallel = TRUE,
+  ncore = 2
+)
+
+# View model summary
+summary.bayesmsm(model)
+```
+
+
+# License
+
+This package is licensed under the MIT License. See the LICENSE file for details.
+
+
+# Citation
+
+Please cite our software using:
+
+```
+@Manual{,
+  title = {bayesmsm: An R package for longitudinal causal analysis using Bayesian Marginal Structural Models},
+  author = {Xiao Yan and Martin Urner and Olli Saarela and Kuan Liu},
+  year = {2024},
+  note = { https://github.com/Kuan-Liu-Lab/bayesmsm},
+  url = {https://kuan-liu-lab.github.io/bayesmsm/},
+}
+```
+
+## Contact
+
+* e-mail: <[email protected]>, <[email protected]>
+
+
+# Getting help or contributing
+
+Please report bugs by opening an
+[issue](https://github.com/Kuan-Liu-Lab/bayesmsm/issues/new). If you have
+a question regarding the usage of `bayesmsm`, please open a
+[discussion](https://github.com/Kuan-Liu-Lab/bayesmsm/discussions/new/choose). 
+If you would like to contribute to the package, please open a pull request.

README.md

@@ -1,22 +1,40 @@
-# R package bayesmsm
 
-This package provides tools for estimating causal effects using Bayesian Marginal Structural Models. It includes functions for estimating Bayesian weights using JAGS and for Bayesian non-parametric bootstrap to calculate causal effects.
+<!-- README.md is generated from README.Rmd. Please edit this file -->
 
+## bayesmsm
 
-# Reference
+<!-- badges: start -->
+<!-- badges: end -->
 
-Reference paper on Bayesian marginal structural models:
+# Overview
+
+*bayesmsm* is an R package that implements the Bayesian marginal
+structrual models to estimate average treatment effect for drawing
+causal inference with time-varying treatment assignment and confoudning
+with extension to handle informative right-censoring. The Bayesian
+marginal structural models is a semi-parametric approach and features a
+two-step estimation process. The first step involves Bayesian parametric
+estimation of the time-varying treatment assignment models and the
+second step involves non-parametric Bayesian bootstrap to estimate the
+average treatment effect between two distinct treatment sequences of
+interest.
 
--  Saarela, O., Stephens, D. A., Moodie, E. E., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288.
+Reference paper on Bayesian marginal structural models:
 
--  Liu, K., Saarela, O., Feldman, B. M., & Pullenayegum, E. (2020). Estimation of causal effects with repeatedly measured outcomes in a Bayesian framework. Statistical methods in medical research, 29(9), 2507-2519.
+- Saarela, O., Stephens, D. A., Moodie, E. E., & Klein, M. B. (2015). On
+  Bayesian estimation of marginal structural models. Biometrics, 71(2),
+  279-288.
 
+- Liu, K., Saarela, O., Feldman, B. M., & Pullenayegum, E. (2020).
+  Estimation of causal effects with repeatedly measured outcomes in a
+  Bayesian framework. Statistical methods in medical research, 29(9),
+  2507-2519.
 
 # Installation
 
 Install using `devtools` package:
 
-```r
+``` r
 ## install.packages(devtools) ## make sure to have devtools installed 
 devtools::install_github("Kuan-Liu-Lab/bayesmsm")
 library(bayesmsm)
@@ -25,19 +43,19 @@ library(bayesmsm)
 # Dependency
 
 This package depends on the following R packages:
--  `MCMCpack`
--  `doParallel`
--  `foreach`
--  `parallel`
--  `R2jags`
--  `coda`
 
+- `MCMCpack`
+- `doParallel`
+- `foreach`
+- `parallel`
+- `R2jags`
+- `coda`
 
-# Examples
+# Quickstart
 
 Here are some examples demonstrating how to use the `bayesmsm` package:
 
-```r
+``` r
 # Load example data
 testdata <- read.csv(system.file("extdata", "continuous_outcome_data.csv", package = "bayesmsm"))
 
@@ -76,23 +94,32 @@ model <- bayesmsm(
 summary.bayesmsm(model)
 ```
 
-
-# Documentation
-
-For comprehensive documentation and examples, visit the [Vignette](https://Kuan-Liu-Lab.github.io/bayesmsm/).
-
-
 # License
 
-This package is licensed under the MIT License. See the LICENSE file for details.
-
+This package is licensed under the MIT License. See the LICENSE file for
+details.
 
 # Citation
 
-If you use the `bayesmsm` package in your research, please cite the reference papers listed above.
+Please cite our software using:
+
+    @Manual{,
+      title = {bayesmsm: An R package for longitudinal causal analysis using Bayesian Marginal Structural Models},
+      author = {Xiao Yan and Martin Urner and Olli Saarela and Kuan Liu},
+      year = {2024},
+      note = { https://github.com/Kuan-Liu-Lab/bayesmsm},
+      url = {https://kuan-liu-lab.github.io/bayesmsm/},
+    }
+
+## Contact
 
+- e-mail: <[email protected]>, <[email protected]>
 
-# Developers
+# Getting help or contributing
 
--  Xiao Yan
--  Kuan Liu
+Please report bugs by opening an
+[issue](https://github.com/Kuan-Liu-Lab/bayesmsm/issues/new). If you
+have a question regarding the usage of `bayesmsm`, please open a
+[discussion](https://github.com/Kuan-Liu-Lab/bayesmsm/discussions/new/choose).
+If you would like to contribute to the package, please open a pull
+request.

_pkgdown.yml

@@ -0,0 +1,54 @@
+url: https://kuan-liu-lab.github.io/bayesmsm/
+
+template:
+  params:
+    bootswatch: cosmo
+
+authors:
+  Kuan Liu:
+    href: http://Kuan-Liu.com/
+  Xiao Yan:
+    href: https://www.linkedin.com/in/xiaoyan1596/
+  Martin Urner:
+    href: https://criticalcaretoronto.com/our-fellows/dr-martin-urner/
+  Olli Saarela:
+    href: http://individual.utoronto.ca/osaarela/
+
+development:
+  mode: release
+
+reference:
+- title: All functions
+  contents:
+    - '`bayesmsm`'
+    - '`bayesweight`'
+    - '`bayesweight_cen`'
+    - '`calculate_effect`'
+    - '`plot_APO`'
+    - '`plot_ATE`'
+    - '`plot_est_box`'
+    - '`summary_bayesmsm`'
+
+navbar:
+  structure:
+    left:
+    - home
+    - Vignettes
+    - Reference
+    - Changelog
+    right: github
+  components:
+    home:
+      icon: fas fa-home fa-lg
+      href: index.html
+    articles:
+      text: Vignettes
+      menu:
+      - text: Bayesian MSMs without censoring
+        href: articles/bayesmsm-vignette.html
+    reference:
+      text: Reference
+      href: reference/index.html
+    news:
+      text: Changelog
+      href: news/index.html