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remove html links from vignettes, instead put web-links
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RafaelVias committed Sep 6, 2024
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4 changes: 2 additions & 2 deletions DESCRIPTION
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Expand Up @@ -26,8 +26,8 @@ Suggests:
covr,
vdiffr
VignetteBuilder: knitr
URL: https://sor16.github.io/bdrc
BugReports: https://github.com/sor16/bdrc/issues
URL: https://sor16.github.io/bdrc/
BugReports: https://github.com/sor16/bdrc/issues/
Encoding: UTF-8
LinkingTo:
Rcpp (>= 0.11.0),
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6 changes: 3 additions & 3 deletions README.Rmd
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Expand Up @@ -64,9 +64,9 @@ plot(gplm.fit)

For a deeper dive into the package's functionality, visualization options, and the underlying theory of the models, please check out our vignettes:

* [Background](background.html)
* [Introduction](introduction.html)
* [Power-law tournament (model selection)](tournament.html)
* [Background](https://sor16.github.io/bdrc/articles/background.html)
* [Introduction](https://sor16.github.io/bdrc/articles/introduction.html)
* [Power-law tournament (model selection)](https://sor16.github.io/bdrc/articles/tournament.html)


## References
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7 changes: 4 additions & 3 deletions README.md
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Expand Up @@ -67,9 +67,10 @@ For a deeper dive into the package’s functionality, visualization
options, and the underlying theory of the models, please check out our
vignettes:

- [Background](background.html)
- [Introduction](introduction.html)
- [Power-law tournament (model selection)](tournament.html)
- [Background](https://sor16.github.io/bdrc/articles/background.html)
- [Introduction](https://sor16.github.io/bdrc/articles/introduction.html)
- [Power-law tournament (model
selection)](https://sor16.github.io/bdrc/articles/tournament.html)

## References

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2 changes: 1 addition & 1 deletion docs/pkgdown.yml
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Expand Up @@ -5,7 +5,7 @@ articles:
background: background.html
introduction: introduction.html
tournament: tournament.html
last_built: 2024-09-04T14:46Z
last_built: 2024-09-06T18:45Z
urls:
reference: https://sor16.github.io/bdrc/reference
article: https://sor16.github.io/bdrc/articles
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2 changes: 1 addition & 1 deletion vignettes/introduction.Rmd
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Expand Up @@ -28,7 +28,7 @@ A discharge rating curve is a model that describes the relationship between wate

* ```gplm() ``` - Generalized power-law model with stage-dependent log-error variance.

For further details about the different models, see Hrafnkelsson et al. (2022). For an brief overview of the underlying theory, see our [Background](background.html) vignette.
For further details about the different models, see Hrafnkelsson et al. (2022). For an brief overview of the underlying theory, see our [Background](https://sor16.github.io/bdrc/articles/background.html) vignette.

The models differ in their complexity, `gplm` being the most flexible and complex model. We will focus on the use of `gplm` throughout this introduction vignette and explore the different ways to fit the `gplm` and visualize its output. However, the API of the functions for the other three models are almost identical so this vignette also helps users to run those models.
```{r,message=FALSE}
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2 changes: 1 addition & 1 deletion vignettes/tournament.Rmd
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Expand Up @@ -31,7 +31,7 @@ This vignette explores the ways you can compare the fit of the different dischar

* ```gplm() ``` - Generalized power-law model with stage-dependent log-error variance.

The '0' indicates that the variance of the log-transformed errors is modeled as a constant. The `plm0` model is simply a Bayesian hierarchical implementation of the classical power-law model, widely used in hydrological practice. For more details on these models, refer to Hrafnkelsson et al. (2022). For an brief overview of the underlying theory, see our [Background](background.html) vignette. For guidance on applying these models to your data, refer to the [Introduction](introduction.html) vignette.
The '0' indicates that the variance of the log-transformed errors is modeled as a constant. The `plm0` model is simply a Bayesian hierarchical implementation of the classical power-law model, widely used in hydrological practice. For more details on these models, refer to Hrafnkelsson et al. (2022). For an brief overview of the underlying theory, see our [Background](https://sor16.github.io/bdrc/articles/background.html) vignette. For guidance on applying these models to your data, refer to the [Introduction](https://sor16.github.io/bdrc/articles/introduction.html) vignette.

## The tournament method

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