Last updated: 2024-12-06

Checks: 2 0

Knit directory: online_tut/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Repository version: d86f3f0

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version d86f3f0. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/.Rhistory
    Ignored:    code/.DS_Store

Untracked files:
    Untracked:  analysis/_includes/

Unstaged changes:
    Modified:   analysis/_site.yml

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html c36b101 Ziang Zhang 2024-11-24 Build site.
Rmd 7e8beb9 Ziang Zhang 2024-11-24 workflowr::wflow_publish("analysis/index.rmd")
html 334b73a Ziang Zhang 2024-11-21 Build site.
Rmd 5d64724 Ziang Zhang 2024-11-21 workflowr::wflow_publish("analysis/index.rmd")
html 46af38c Ziang Zhang 2024-11-21 Build site.
html 5c887d7 Ziang Zhang 2024-11-21 Build site.
html b02cf36 Ziang Zhang 2024-11-21 Build site.
html 67c90b4 Ziang Zhang 2024-11-21 Build site.
Rmd 0501267 Ziang Zhang 2024-11-21 workflowr::wflow_publish("analysis/index.rmd")
html 5ec276f Ziang Zhang 2024-11-21 Build site.
Rmd be24a4b Ziang Zhang 2024-11-21 workflowr::wflow_publish("analysis/index.rmd")
html c401920 Ziang Zhang 2024-11-21 Build site.
Rmd 2a37224 Ziang Zhang 2024-11-21 workflowr::wflow_publish("analysis/index.rmd")
Rmd bb32e24 Ziang Zhang 2024-11-20 Start workflowr project.

For an overview of the sGP model and its computational methods, see this paper.

BayesGP, available on CRAN, efficiently implements Gaussian process priors (including the sGP) for a variety of Bayesian hierarchical models.

sGPfit, available on GitHub, is designed to help with more advanced applications of the sGP, including fitting the exact process via the state-space representation.

Tutorials:

These tutorials are organized progressively, starting with basic concepts and moving to more advanced topics. We recommend beginning with the first tutorial and proceeding in order.

Background Readings:

Our implementation of the sGP model is grounded in the framework of Bayesian hierarchical models, specifically the Extended Latent Gaussian Models (ELGMs). Approximate Bayesian inference for ELGMs typically relies on the Laplace approximation and Adaptive Gauss-Hermite Quadrature (AGHQ).

For readers less familiar with these concepts, we recommend the following resources:

Additionally, for readers seeking more tutorials on the SPDE framework, from which the sGP prior is derived, we recommend this work by Miller et al. (2020).