From dbf4381b6056fe1046575d0fdbbd553666beeacb Mon Sep 17 00:00:00 2001
From: bayesianstvc <44657539+bayesianstvc@users.noreply.github.com>
Date: Tue, 28 Jul 2026 09:05:37 +0800
Subject: [PATCH 1/3] docs: update BSTVC homepage links and tools
---
README.Rmd | 24 +++-
README.md | 360 +++++++++++++++++++++++++++--------------------------
2 files changed, 204 insertions(+), 180 deletions(-)
diff --git a/README.Rmd b/README.Rmd
index 2fc1a07..c8122ac 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -20,11 +20,10 @@ knitr::opts_chunk$set(
-
-
+
[](https://doi.org/10.1016/j.ijdrr.2022.103078)
@@ -45,11 +44,11 @@ only supports local installation from GitHub.
``` r
# Install using the devtools package
# install.packages("devtools")
-devtools::install_github("songbi123/BSTVC")
+devtools::install_github("bayesianstvc/BSTVC")
# Install using the remotes package
# install.packages("remotes")
-remotes::install_github("songbi123/BSTVC")
+remotes::install_github("bayesianstvc/BSTVC")
```
**- Install the dependency package - the `INLA` R package**
@@ -74,6 +73,19 @@ install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-dow
## Another approach, using an older version of INLA, is to download the compressed package of the INLA R package to your local machine and then proceed with the installation.
```
+
+## Desktop applications and companion tools
+
+### Chinese desktop version — BSTVC Desktop (时空可解释工具)
+
+The Chinese desktop version, **BSTVC桌面版（时空可解释工具）**, provides a more accessible graphical workflow for spatiotemporal analysis and interpretation. Visit the [official website](https://bayesianstvc.github.io/BSTVC-R/) for the latest introduction and access information.
+
+The English desktop version is coming soon — stay tuned.
+
+### INLA Process Monitor
+
+[INLA Process Monitor](https://github.com/bayesianstvc/inla-monitor) is a Windows companion tool for monitoring live inla.exe resource usage and comparing CPU, memory, and thread behavior. It helps users identify an appropriate thread setting and diagnose performance during Bayesian latent Gaussian model fitting. The tool runs locally and does not require a cloud service.
+
## Features & Advantages
The `BSTVC` R package is designed to provide a comprehensive suite of
@@ -126,7 +138,7 @@ guides, as follows:
| Guide | Details |
|-------------------|------------------------------|
-| **User's Guide for the BSTVC R Package** | This usage guide covers detailed example operations and important considerations for each key step, including data import, inspection, preprocessing, model fitting, result output and result visualization. You can view it in the `GetStart.Rmd` document under the `vignettes` folder, but it's in R markdown format.
If you want to download the help document in PDF format, please click [here](https://github.com/songbi123/BSTVC/raw/songbi123-useguides/GetStart.pdf), the filename is [GetStart-English.pdf](https://github.com/songbi123/BSTVC/raw/songbi123-useguides/GetStart.pdf). At the same time, to meet the needs of Chinese users, we have also provided a Chinese version of the usage guide, which can be downloaded and saved locally by visiting [用户手册-中文版.pdf](https://github.com/songbi123/BSTVC/raw/songbi123-useguides/GetStart-Chinese.pdf). |
+| **User's Guide for the BSTVC R Package** | This usage guide covers detailed example operations and important considerations for each key step, including data import, inspection, preprocessing, model fitting, result output and result visualization. You can view it in the `GetStart.Rmd` document under the `vignettes` folder, but it's in R markdown format.
If you want to download the help document in PDF format, please click [here](https://github.com/bayesianstvc/BSTVC/raw/songbi123-useguides/GetStart.pdf), the filename is [GetStart-English.pdf](https://github.com/bayesianstvc/BSTVC/raw/songbi123-useguides/GetStart.pdf). At the same time, to meet the needs of Chinese users, we have also provided a Chinese version of the usage guide, which can be downloaded and saved locally by visiting [用户手册-中文版.pdf](https://github.com/bayesianstvc/BSTVC/raw/songbi123-useguides/GetStart-Chinese.pdf). |
| **Modeling Data Processing Guide** | This usage guide demonstrates how to import the types of data required for the model and how to transform the raw data into the spatiotemporal panel data format that can be processed by the BSTVC model. The R code for achieving data processing for modeling is located in the `Data_Preproc.R` file under the `data-raw` folder. |
In the near future, we will continue to refine our documentation and
@@ -143,7 +155,7 @@ requesting new features, or submitting code changes. If you encounter
any problems when using the BSTVC package or need further assistance,
you can get support through the following means:
- 1. **GitHub issues**: Report issues or request new features in theGitHub repository, please visit [Issues](https://github.com/songbi123/BSTVC/issues).
+ 1. **GitHub issues**: Report issues or request new features in theGitHub repository, please visit [Issues](https://github.com/bayesianstvc/BSTVC/issues).
2. **Email contact**: [tangxxxxt\@163.com](mailto:tangxxxxt@163.com)(Tang Xianteng, related to R package usage); [chaosong.gis\@gmail.com](mailto:chaosong.gis@gmail.com) (Song Chao, related to statistical theory)
3. **Bayesian STVC model**:
diff --git a/README.md b/README.md
index 041b525..d5c4b96 100644
--- a/README.md
+++ b/README.md
@@ -1,174 +1,186 @@
-
-
-
-# BSTVC 
-
-
-
-
-
-
-
-
-[](https://doi.org/10.1016/j.ijdrr.2022.103078)
-
-
-
-**Spatiotemporal heterogeneous perspective for analyzing influencing
-factors, identifying key drivers, and making dynamic predictions, all
-within a unified ‘full-map’ framework.**
-
-
-
-
-## Installation
-
-**- Install the `BSTVC` R package**
-
-The package is currently in the internal testing phase. At present, it
-only supports local installation from GitHub.
-
-``` r
-# Install using the devtools package
-# install.packages("devtools")
-devtools::install_github("songbi123/BSTVC")
-
-# Install using the remotes package
-# install.packages("remotes")
-remotes::install_github("songbi123/BSTVC")
-```
-
-**- Install the dependency package - the `INLA` R package**
-
-When installing the `BSTVC` package in RStudio, the system will prompt
-you to install additional R packages that come with it. However, since
-`INLA` is a larger package, installing the `BSTVC` package might lead to
-a failure. To avoid this issue, we provide a separate method for
-installing the `INLA` package for your reference.
-
-If the installation of the `BSTVC` package in the previous step failed,
-please install the `BSTVC` package after successfully installing the
-`INLA` package. If you have successfully installed the `INLA` package
-while installing the `BSTVC` package, you can skip this step.
-
-``` r
-## To install the INLA package, more information can be found at .
-# Extend the overtime duration to 5 minutes
-options(timeout = 300)
-install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)
-
-## Another approach, using an older version of INLA, is to download the compressed package of the INLA R package to your local machine and then proceed with the installation.
-```
-
-## Features & Advantages
-
-The `BSTVC` R package is designed to provide a comprehensive suite of
-functionalities for advanced spatiotemporal heterogeneous analysis.
-Here’s what our package can do for you:
-
-- **Targeting multiple types of response variables**: It supports three
- mainstream types of response variables: continuous (log-Gaussian
- regression), binary (logistic regression), and count (Poisson
- regression), accommodating various analytical scenarios.
-
-- **Detecting spatiotemporal heterogeneous impact mechanisms**: By
- fitting spatiotemporal regression coefficients, it reveals local
- spatiotemporal differences between explanatory variables (X) and
- response variables (Y), facilitating an in-depth analysis of
- context-specific patterns and exploring the impact mechanisms brought
- by spatiotemporal heterogeneity.
-
-- **Identifying spatiotemporal driving factors**: On the basis of
- identifying spatiotemporal heterogeneous impact mechanisms, it
- clarifies key driving factors by calculating the spatiotemporal
- explainable percentage, providing strong evidence for geographical
- spatiotemporal attribution.
-
-- **Improving spatiotemporal prediction accuracy**: Considering the
- spatiotemporal heterogeneity of local variable relationships, it
- significantly improves model fitting and prediction accuracy, which
- can be used for spatiotemporal missing value imputation,
- spatiotemporal smoothing, and future forecasting.
-
-- **Bayesian model assessment**: It provides a comprehensive evaluation
- of Bayesian regression models, including model fitting (DIC, WAIC),
- complexity (pd), and prediction accuracy (LS) indicators, helping
- users fully understand model performance.
-
-- **Rich visualization outputs**: It provides a variety of
- spatiotemporal visualization tools and codes to help users intuitively
- understand model results, enhance the interpretability of data
- analysis, and promote innovation in your applied research.
-
-Bayesian STVC model is a powerful analytical tool with many advantages
-that other similar tools lack, such as **a *“full-map” modeling
-framework*, *parameter uncertainty*, *friendliness to missing values*,
-and *support for more spatial weight matrices***, among others.
-
-## Usage Guide
-
-To help you quickly and fully get started with our R package for complex
-data analysis, we have prepared several detailed and comprehensive usage
-guides, as follows:
-
-| Guide | Details |
-|----|----|
-| **User’s Guide for the BSTVC R Package** | This usage guide covers detailed example operations and important considerations for each key step, including data import, inspection, preprocessing, model fitting, result output and result visualization. You can view it in the `GetStart.Rmd` document under the `vignettes` folder, but it’s in R markdown format.
If you want to download the help document in PDF format, please click [here](https://github.com/songbi123/BSTVC/raw/songbi123-useguides/GetStart.pdf), the filename is [GetStart-English.pdf](https://github.com/songbi123/BSTVC/raw/songbi123-useguides/GetStart.pdf). At the same time, to meet the needs of Chinese users, we have also provided a Chinese version of the usage guide, which can be downloaded and saved locally by visiting [用户手册-中文版.pdf](https://github.com/songbi123/BSTVC/raw/songbi123-useguides/GetStart-Chinese.pdf). |
-| **Modeling Data Processing Guide** | This usage guide demonstrates how to import the types of data required for the model and how to transform the raw data into the spatiotemporal panel data format that can be processed by the BSTVC model. The R code for achieving data processing for modeling is located in the `Data_Preproc.R` file under the `data-raw` folder. |
-
-In the near future, we will continue to refine our documentation and
-provide new help documents.
-
-## Changelog
-
-View detailed changelog: [CHANGELOG.md](./CHANGELOG.md)
-
-## Contact
-
-We welcome and encourage user contributions, including reporting issues,
-requesting new features, or submitting code changes. If you encounter
-any problems when using the BSTVC package or need further assistance,
-you can get support through the following means:
-
-1. **GitHub issues**: Report issues or request new features in
- theGitHub repository, please visit
- [Issues](https://github.com/songbi123/BSTVC/issues).
-2. **Email contact**: (Tang Xianteng, related to R
- package usage); (Song Chao, related to
- statistical theory)
-3. **Bayesian STVC model**:
-
-Copyright **©HEOA-West China Health and Medical Geography Research
-Group**
-
-If you are a WeChat user, you are welcome to scan the QR code to follow
-our research group’s official account: **HealthGeography**
-
-
-
-## Reference
-
-- **\[Bayesian STVC series models\]** Song, Chao, Yin, Hao, Shi, Xun,
- Xie, Mingyu, Yang, Shujuan, Zhou, Junmin, Wang, Xiuli, Tang,
- Zhangying, Yang, Yili, & Pan, Jay. (2022). Spatiotemporal disparities
- in regional public risk perception of COVID-19 using Bayesian
- Spatiotemporally Varying Coefficients (STVC) series models across
- Chinese cities. *International Journal of Disaster Risk Reduction*,
- 77, 103078.
-
-- **\[STVPI\]** Wan, Qin, Tang, Zhangying, Pan, Jay, Xie, Mingyu, Wang,
- Shaobin, Yin, Hao, Li, Junmin, Liu, Xin, Yang, Yang, & Song, Chao.
- (2022). Spatiotemporal heterogeneity in associations of national
- population ageing with socioeconomic and environmental factors at the
- global scale. *Journal of Cleaner Production*, 373, 133781.
-
-- Song, Chao, Shi, Xun, & Wang, Jinfeng. (2020). Spatiotemporally
- Varying Coefficients (STVC) model: a Bayesian local regression to
- detect spatial and temporal nonstationarity in variables
- relationships. *Annals of GIS*, 26(3), 277-291.
-
-- Song, Chao, Shi, Xun, Bo, Yanchen, Wang, Jinfeng, Wang, Yong, & Huang,
- Dacang. (2019). Exploring Spatiotemporal Nonstationary Effects of
- Climate Factors on Hand, Foot, and Mouth Disease Using Bayesian
- Spatiotemporally Varying Coefficients (STVC) Model in Sichuan, China.
- *Science of The Total Environment*, 648, 550-560.
+
+
+
+# BSTVC
+
+
+
+
+
+
+
+[](https://doi.org/10.1016/j.ijdrr.2022.103078)
+
+
+
+**Spatiotemporal heterogeneous perspective for analyzing influencing
+factors, identifying key drivers, and making dynamic predictions, all
+within a unified ‘full-map’ framework.**
+
+
+
+
+## Installation
+
+**- Install the `BSTVC` R package**
+
+The package is currently in the internal testing phase. At present, it
+only supports local installation from GitHub.
+
+``` r
+# Install using the devtools package
+# install.packages("devtools")
+devtools::install_github("bayesianstvc/BSTVC")
+
+# Install using the remotes package
+# install.packages("remotes")
+remotes::install_github("bayesianstvc/BSTVC")
+```
+
+**- Install the dependency package - the `INLA` R package**
+
+When installing the `BSTVC` package in RStudio, the system will prompt
+you to install additional R packages that come with it. However, since
+`INLA` is a larger package, installing the `BSTVC` package might lead to
+a failure. To avoid this issue, we provide a separate method for
+installing the `INLA` package for your reference.
+
+If the installation of the `BSTVC` package in the previous step failed,
+please install the `BSTVC` package after successfully installing the
+`INLA` package. If you have successfully installed the `INLA` package
+while installing the `BSTVC` package, you can skip this step.
+
+``` r
+## To install the INLA package, more information can be found at .
+# Extend the overtime duration to 5 minutes
+options(timeout = 300)
+install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)
+
+## Another approach, using an older version of INLA, is to download the compressed package of the INLA R package to your local machine and then proceed with the installation.
+```
+
+
+## Desktop applications and companion tools
+
+### Chinese desktop version — BSTVC Desktop (时空可解释工具)
+
+The Chinese desktop version, **BSTVC桌面版（时空可解释工具）**, provides a more accessible graphical workflow for spatiotemporal analysis and interpretation. Visit the [official website](https://bayesianstvc.github.io/BSTVC-R/) for the latest introduction and access information.
+
+The English desktop version is coming soon — stay tuned.
+
+### INLA Process Monitor
+
+[INLA Process Monitor](https://github.com/bayesianstvc/inla-monitor) is a Windows companion tool for monitoring live inla.exe resource usage and comparing CPU, memory, and thread behavior. It helps users identify an appropriate thread setting and diagnose performance during Bayesian latent Gaussian model fitting. The tool runs locally and does not require a cloud service.
+
+## Features & Advantages
+
+The `BSTVC` R package is designed to provide a comprehensive suite of
+functionalities for advanced spatiotemporal heterogeneous analysis.
+Here’s what our package can do for you:
+
+- **Targeting multiple types of response variables**: It supports three
+ mainstream types of response variables: continuous (log-Gaussian
+ regression), binary (logistic regression), and count (Poisson
+ regression), accommodating various analytical scenarios.
+
+- **Detecting spatiotemporal heterogeneous impact mechanisms**: By
+ fitting spatiotemporal regression coefficients, it reveals local
+ spatiotemporal differences between explanatory variables (X) and
+ response variables (Y), facilitating an in-depth analysis of
+ context-specific patterns and exploring the impact mechanisms brought
+ by spatiotemporal heterogeneity.
+
+- **Identifying spatiotemporal driving factors**: On the basis of
+ identifying spatiotemporal heterogeneous impact mechanisms, it
+ clarifies key driving factors by calculating the spatiotemporal
+ explainable percentage, providing strong evidence for geographical
+ spatiotemporal attribution.
+
+- **Improving spatiotemporal prediction accuracy**: Considering the
+ spatiotemporal heterogeneity of local variable relationships, it
+ significantly improves model fitting and prediction accuracy, which
+ can be used for spatiotemporal missing value imputation,
+ spatiotemporal smoothing, and future forecasting.
+
+- **Bayesian model assessment**: It provides a comprehensive evaluation
+ of Bayesian regression models, including model fitting (DIC, WAIC),
+ complexity (pd), and prediction accuracy (LS) indicators, helping
+ users fully understand model performance.
+
+- **Rich visualization outputs**: It provides a variety of
+ spatiotemporal visualization tools and codes to help users intuitively
+ understand model results, enhance the interpretability of data
+ analysis, and promote innovation in your applied research.
+
+Bayesian STVC model is a powerful analytical tool with many advantages
+that other similar tools lack, such as **a *“full-map” modeling
+framework*, *parameter uncertainty*, *friendliness to missing values*,
+and *support for more spatial weight matrices***, among others.
+
+## Usage Guide
+
+To help you quickly and fully get started with our R package for complex
+data analysis, we have prepared several detailed and comprehensive usage
+guides, as follows:
+
+| Guide | Details |
+|----|----|
+| **User’s Guide for the BSTVC R Package** | This usage guide covers detailed example operations and important considerations for each key step, including data import, inspection, preprocessing, model fitting, result output and result visualization. You can view it in the `GetStart.Rmd` document under the `vignettes` folder, but it’s in R markdown format.
If you want to download the help document in PDF format, please click [here](https://github.com/bayesianstvc/BSTVC/raw/songbi123-useguides/GetStart.pdf), the filename is [GetStart-English.pdf](https://github.com/bayesianstvc/BSTVC/raw/songbi123-useguides/GetStart.pdf). At the same time, to meet the needs of Chinese users, we have also provided a Chinese version of the usage guide, which can be downloaded and saved locally by visiting [用户手册-中文版.pdf](https://github.com/bayesianstvc/BSTVC/raw/songbi123-useguides/GetStart-Chinese.pdf). |
+| **Modeling Data Processing Guide** | This usage guide demonstrates how to import the types of data required for the model and how to transform the raw data into the spatiotemporal panel data format that can be processed by the BSTVC model. The R code for achieving data processing for modeling is located in the `Data_Preproc.R` file under the `data-raw` folder. |
+
+In the near future, we will continue to refine our documentation and
+provide new help documents.
+
+## Changelog
+
+View detailed changelog: [CHANGELOG.md](./CHANGELOG.md)
+
+## Contact
+
+We welcome and encourage user contributions, including reporting issues,
+requesting new features, or submitting code changes. If you encounter
+any problems when using the BSTVC package or need further assistance,
+you can get support through the following means:
+
+1. **GitHub issues**: Report issues or request new features in
+ theGitHub repository, please visit
+ [Issues](https://github.com/bayesianstvc/BSTVC/issues).
+2. **Email contact**: (Tang Xianteng, related to R
+ package usage); (Song Chao, related to
+ statistical theory)
+3. **Bayesian STVC model**:
+
+Copyright **©HEOA-West China Health and Medical Geography Research
+Group**
+
+If you are a WeChat user, you are welcome to scan the QR code to follow
+our research group’s official account: **HealthGeography**
+
+
+
+## Reference
+
+- **\[Bayesian STVC series models\]** Song, Chao, Yin, Hao, Shi, Xun,
+ Xie, Mingyu, Yang, Shujuan, Zhou, Junmin, Wang, Xiuli, Tang,
+ Zhangying, Yang, Yili, & Pan, Jay. (2022). Spatiotemporal disparities
+ in regional public risk perception of COVID-19 using Bayesian
+ Spatiotemporally Varying Coefficients (STVC) series models across
+ Chinese cities. *International Journal of Disaster Risk Reduction*,
+ 77, 103078.
+
+- **\[STVPI\]** Wan, Qin, Tang, Zhangying, Pan, Jay, Xie, Mingyu, Wang,
+ Shaobin, Yin, Hao, Li, Junmin, Liu, Xin, Yang, Yang, & Song, Chao.
+ (2022). Spatiotemporal heterogeneity in associations of national
+ population ageing with socioeconomic and environmental factors at the
+ global scale. *Journal of Cleaner Production*, 373, 133781.
+
+- Song, Chao, Shi, Xun, & Wang, Jinfeng. (2020). Spatiotemporally
+ Varying Coefficients (STVC) model: a Bayesian local regression to
+ detect spatial and temporal nonstationarity in variables
+ relationships. *Annals of GIS*, 26(3), 277-291.
+
+- Song, Chao, Shi, Xun, Bo, Yanchen, Wang, Jinfeng, Wang, Yong, & Huang,
+ Dacang. (2019). Exploring Spatiotemporal Nonstationary Effects of
+ Climate Factors on Hand, Foot, and Mouth Disease Using Bayesian
+ Spatiotemporally Varying Coefficients (STVC) Model in Sichuan, China.
+ *Science of The Total Environment*, 648, 550-560.
From 5fee474d0e112b96ccb92eb6b3853de15fd0aaad Mon Sep 17 00:00:00 2001
From: bayesianstvc <44657539+bayesianstvc@users.noreply.github.com>
Date: Tue, 28 Jul 2026 10:06:45 +0800
Subject: [PATCH 2/3] docs: refine README visuals and tool placement
---
README.Rmd | 20 +++++++++++---------
README.md | 20 +++++++++++---------
man/Figure/BSTVC_desktop_logo.png | Bin 0 -> 91398 bytes
3 files changed, 22 insertions(+), 18 deletions(-)
create mode 100644 man/Figure/BSTVC_desktop_logo.png
diff --git a/README.Rmd b/README.Rmd
index c8122ac..b7d1fc7 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -16,13 +16,11 @@ knitr::opts_chunk$set(
)
```
-# BSTVC
+# BSTVC
-
-
[](https://doi.org/10.1016/j.ijdrr.2022.103078)
@@ -74,7 +72,9 @@ install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-dow
```
-## Desktop applications and companion tools
+## BSTVC Desktop
+
+
### Chinese desktop version — BSTVC Desktop (时空可解释工具)
@@ -82,10 +82,6 @@ The Chinese desktop version, **BSTVC桌面版（时空可解释工具）**, prov
The English desktop version is coming soon — stay tuned.
-### INLA Process Monitor
-
-[INLA Process Monitor](https://github.com/bayesianstvc/inla-monitor) is a Windows companion tool for monitoring live inla.exe resource usage and comparing CPU, memory, and thread behavior. It helps users identify an appropriate thread setting and diagnose performance during Bayesian latent Gaussian model fitting. The tool runs locally and does not require a cloud service.
-
## Features & Advantages
The `BSTVC` R package is designed to provide a comprehensive suite of
@@ -148,6 +144,12 @@ provide new help documents.
View detailed changelog: [CHANGELOG.md](./CHANGELOG.md)
+## Additional companion tool
+
+### INLA Process Monitor
+
+[INLA Process Monitor](https://github.com/bayesianstvc/inla-monitor) is a Windows companion tool for monitoring live inla.exe resource usage and comparing CPU, memory, and thread behavior. It helps users identify an appropriate thread setting and diagnose performance during Bayesian latent Gaussian model fitting. The tool runs locally and does not require a cloud service.
+
## Contact
We welcome and encourage user contributions, including reporting issues,
@@ -163,7 +165,7 @@ Copyright **©HEOA-West China Health and Medical Geography Research Group**
If you are a WeChat user, you are welcome to scan the QR code to follow our research group's official account: **HealthGeography**
-
+
## Reference
diff --git a/README.md b/README.md
index d5c4b96..fb5529e 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,11 @@
-# BSTVC
+# BSTVC
-
-
[](https://doi.org/10.1016/j.ijdrr.2022.103078)
@@ -60,7 +58,9 @@ install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-dow
```
-## Desktop applications and companion tools
+## BSTVC Desktop
+
+
### Chinese desktop version — BSTVC Desktop (时空可解释工具)
@@ -68,10 +68,6 @@ The Chinese desktop version, **BSTVC桌面版（时空可解释工具）**, prov
The English desktop version is coming soon — stay tuned.
-### INLA Process Monitor
-
-[INLA Process Monitor](https://github.com/bayesianstvc/inla-monitor) is a Windows companion tool for monitoring live inla.exe resource usage and comparing CPU, memory, and thread behavior. It helps users identify an appropriate thread setting and diagnose performance during Bayesian latent Gaussian model fitting. The tool runs locally and does not require a cloud service.
-
## Features & Advantages
The `BSTVC` R package is designed to provide a comprehensive suite of
@@ -135,6 +131,12 @@ provide new help documents.
View detailed changelog: [CHANGELOG.md](./CHANGELOG.md)
+## Additional companion tool
+
+### INLA Process Monitor
+
+[INLA Process Monitor](https://github.com/bayesianstvc/inla-monitor) is a Windows companion tool for monitoring live inla.exe resource usage and comparing CPU, memory, and thread behavior. It helps users identify an appropriate thread setting and diagnose performance during Bayesian latent Gaussian model fitting. The tool runs locally and does not require a cloud service.
+
## Contact
We welcome and encourage user contributions, including reporting issues,
@@ -156,7 +158,7 @@ Group**
If you are a WeChat user, you are welcome to scan the QR code to follow
our research group’s official account: **HealthGeography**
-
+
## Reference
diff --git a/man/Figure/BSTVC_desktop_logo.png b/man/Figure/BSTVC_desktop_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..81a6f9ca19803f2c0db1b1a2ac68ff9351b4035e
GIT binary patch
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