Last updated: 2024-11-21

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: 0501267

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 0501267. 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:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    code/.DS_Store

Untracked files:
    Untracked:  code/functions.R
    Untracked:  code/tut.cpp
    Untracked:  code/tut.o
    Untracked:  code/tut.so

Unstaged changes:
    Deleted:    analysis/license.Rmd
    Modified:   analysis/lynx.rmd

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
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.