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Automatic README update (#754)
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Expand Up @@ -90,6 +90,20 @@ cost of the granularity of estimates or real-time performance, include:
- Adjustment for the remaining susceptible population beyond the
forecast horizon.

By default, all these models are fit with [MCMC
sampling](https://mc-stan.org/docs/reference-manual/mcmc.html) using the
[`rstan`](https://mc-stan.org/users/interfaces/rstan) R package as the
backend. Users can, however, switch to use approximate algorithms like
[variational
inference](https://en.wikipedia.org/wiki/Variational_Bayesian_methods),
the
[pathfinder](https://mc-stan.org/docs/reference-manual/pathfinder.html)
algorithm, or [Laplace
approximation](https://mc-stan.org/docs/reference-manual/laplace.html)
especially for quick prototyping. The latter two methods are provided
through the [`cmdstanr`](https://mc-stan.org/cmdstanr/) R package, so
users will have to install that separately.

The documentation for `estimate_infections` provides examples of the
implementation of the different options available.

Expand Down Expand Up @@ -175,6 +189,12 @@ the two main functions in the package and how to set up them up. It also
discusses how to summarise and visualise the results after running the
models.

More broadly, users can also learn the details of estimating delay
distributions, nowcasting, and forecasting in a structured way through
the free and open short-course, [“Nowcasting and forecasting infectious
disease dynamics”](https://nfidd.github.io/nfidd/), developed by some
authors of this package.

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