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alxsrobert requested changes Aug 7, 2025
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I just suggested a couple of minor edits, otherwise it all looks good to me.
| - The models' C++ code is in two levels that underlie the R source code - the C++ source code and the C++ headers. This is to make the header code (the model ODEs and helper functions) shareable so that they can be re-used in other Rcpp packages and [this is explored more in this blog post.](https://epiverse-trace.github.io/posts/share-cpp/) | ||
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| - _epidemics_ defines C++ namespaces in the package headers to more clearly indicate where certain functionalities sit. All model structures are in the namespace `epidemics`, while the namespaces `vaccination`, `intervention`, and `time_dependence` contain C++ code to handle these composable elements (such as calculating the effect of cumulative interventions). The namespace `odetools` defines the initial state type, which is an `Eigen::MatrixXd`. | ||
| - A common method for defining and solving ODEs in R is to write the ODE system in R and pass it to solvers such as `deSolve::lsoda()` from the [_deSolve_ package](https://cran.r-project.org/package=deSolve). We have adapted this approach to use the _odin_. The advantage of _odin_ is that it [generates systems of ordinary differential equations (ODE) and integrate them, using a domain specific language (DSL). The DSL uses R's syntax, but compiles to C in order to efficiently solve the system](https://cran.r-project.org/web/packages/odin/index.html) using deSolve. |
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| - A common method for defining and solving ODEs in R is to write the ODE system in R and pass it to solvers such as `deSolve::lsoda()` from the [_deSolve_ package](https://cran.r-project.org/package=deSolve). We have adapted this approach to use the _odin_. The advantage of _odin_ is that it [generates systems of ordinary differential equations (ODE) and integrate them, using a domain specific language (DSL). The DSL uses R's syntax, but compiles to C in order to efficiently solve the system](https://cran.r-project.org/web/packages/odin/index.html) using deSolve. | |
| - A common method for defining and solving ODEs in R is to write the ODE system in R and pass it to solvers such as `deSolve::lsoda()` from the [_deSolve_ package](https://cran.r-project.org/package=deSolve). We have adapted this approach to use the R package [_odin_](https://cran.r-project.org/web/packages/odin/index.html). The advantage of _odin_ is that it generates systems of ordinary differential equations (ODE) and integrate them, using a domain specific language (DSL). The DSL uses R's syntax, but compiles to C in order to efficiently solve the system using deSolve. |
| - Interventions may be of the `<contacts_intervention>` or `<rate_intervention>` class, which inherit from the abstract super-class `<intervention>`. This inheritance structure was chosen to maintain coherence between the intervention types, and to keep the option open of unifying these classes into a single concrete type in the future. | ||
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| - All composable elements except the `population` are optional. Model functions internally generate dummy values for the other composable elements allowed for each model, as these are required for the C++ code. | ||
| - All composable elements except the `population` are optional. Model functions internally generate dummy values for the other composable elements allowed for each model. |
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| - All composable elements except the `population` are optional. Model functions internally generate dummy values for the other composable elements allowed for each model. | |
| - All composable elements except `population` are optional. Model functions internally generate dummy values for the other composable elements allowed for each model. |
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This PR updates the design principles vignette to reflect the odin changes.
It also standardizes the formatting of package names as italics when referenced in documentation and givnettes.