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---
output: github_document
always_allow_html: true
---
# mvgam
> **M**ulti**V**ariate (Dynamic) **G**eneralized **A**dditive **M**odels
The goal of the `mvgam` 📦 is to fit Bayesian Dynamic Generalized Additive Models (DGAMs) that can include highly flexible nonlinear predictor effects for both process and observation components. The package does this by relying on functionalities from the impressive [`brms`](https://paulbuerkner.com/brms/){target="_blank"} and [`mgcv`](https://cran.r-project.org/package=mgcv){target="_blank"} packages. Parameters are estimated using the probabilistic programming language [`Stan`](https://mc-stan.org/), giving users access to the most advanced Bayesian inference algorithms available. This allows `mvgam` to fit a very wide range of models, including:
* [Multivariate State-Space Time Series Models](https://nicholasjclark.github.io/mvgam/articles/trend_formulas.html){target="_blank"}
* [Hierarchical N-mixture Models](https://nicholasjclark.github.io/mvgam/articles/nmixtures.html){target="_blank"}
* [Hierarchical Generalized Additive Models](https://www.youtube.com/watch?v=2POK_FVwCHk){target="_blank"}
* [Joint Species Distribution Models](https://nicholasjclark.github.io/mvgam/reference/jsdgam.html){target="_blank"}
## Installation
Install the stable version from `CRAN` using: `install.packages('mvgam')`, or install the development version from `GitHub` using:
`devtools::install_github("nicholasjclark/mvgam")`. You will also need a working version of `Stan` installed (along with either `rstan` and/or `cmdstanr`). Please refer to installation links for `Stan` with `rstan` [here](https://mc-stan.org/users/interfaces/rstan){target="_blank"}, or for `Stan` with `cmdstandr` [here](https://mc-stan.org/cmdstanr/){target="_blank"}.
## Introductory seminar
<center>
<div style="display: flex; justify-content: center;">
<iframe style="aspect-ratio: 16 / 9; width: 100% !important;" src="https://www.youtube.com/embed/0zZopLlomsQ?si=fWBVTPRDMi9TXcIy" data-external = "1" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</center>
</div>
## Cheatsheet
[](https://github.com/nicholasjclark/mvgam/raw/master/misc/mvgam_cheatsheet.pdf)
## Getting started
`mvgam` was originally designed to analyse and forecast non-negative integer-valued data (counts). These data are traditionally challenging to analyse with existing time-series analysis packages. But further development of `mvgam` has resulted in support for a growing number of observation families that extend to other types of data. Currently, the package can handle data for the following families:
* `gaussian()` for real-valued data
* `student_t()` for heavy-tailed real-valued data
* `lognormal()` for non-negative real-valued data
* `Gamma()` for non-negative real-valued data
* `betar()` for proportional data on `(0,1)`
* `bernoulli()` for binary data
* `poisson()` for count data
* `nb()` for overdispersed count data
* `binomial()` for count data with known number of trials
* `beta_binomial()` for overdispersed count data with known number of trials
* `nmix()` for count data with imperfect detection (unknown number of trials)
See `?mvgam_families` for more information. Below is a simple example for simulating and modelling proportional data with `Beta` observations over a set of seasonal series with independent Gaussian Process dynamic trends:
```{r, include = FALSE}
library(mvgam)
```
```{r}
set.seed(100)
data <- sim_mvgam(
family = betar(),
T = 80,
trend_model = GP(),
prop_trend = 0.5,
seasonality = 'shared'
)
```
Plot the series to see how they evolve over time
```{r, eval = FALSE}
plot_mvgam_series(
data = data$data_train,
series = 'all'
)
```
Fit a State-Space GAM to these series that uses a hierarchical cyclic seasonal smooth term to capture variation in seasonality among series. The model also includes series-specific latent Gaussian Processes with squared exponential covariance functions to capture temporal dynamics
```{r, eval = FALSE}
mod <- mvgam(
y ~ s(season, bs = 'cc', k = 7) +
s(season, by = series, m = 1, k = 5),
trend_model = GP(),
data = data$data_train,
newdata = data$data_test,
family = betar()
)
```
Plot the estimated posterior hindcast and forecast distributions for each series
```{r eval = FALSE}
library(patchwork)
fc <- forecast(mod)
wrap_plots(
plot(fc, series = 1),
plot(fc, series = 2),
plot(fc, series = 3),
ncol = 2
)
```
Various `S3` functions can be used to inspect parameter estimates, plot smooth functions and residuals, and evaluate models through posterior predictive checks or forecast comparisons. Please see [the package documentation](https://nicholasjclark.github.io/mvgam/reference/index.html) for more detailed examples.
## Vignettes
You can set `build_vignettes = TRUE` when installing but be aware this will slow down the installation drastically. Instead, you can always access the vignette htmls online at [https://nicholasjclark.github.io/mvgam/articles/](https://nicholasjclark.github.io/mvgam/articles/)
## Citing `mvgam` and related software
When using any software please make sure to appropriately acknowledge the hard work that developers and maintainers put into making these packages available. Citations are currently the best way to formally acknowledge this work (but feel free to ⭐ [the repo](https://github.com/nicholasjclark/mvgam) as well), so we highly encourage you to cite any packages that you rely on for your research.
When using `mvgam`, please cite the following:
> Clark, N.J. and Wells, K. (2023). Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series. *Methods in Ecology and Evolution*. DOI: https://doi.org/10.1111/2041-210X.13974
As `mvgam` acts as an interface to `Stan`, please additionally cite:
> Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B., Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017). Stan: A probabilistic programming language. *Journal of Statistical Software*. 76(1). DOI: https://doi.org/10.18637/jss.v076.i01
`mvgam` relies on several other `R` packages and, of course, on `R` itself. To find out how to cite `R` and its packages, use `citation()`. There are
some features of `mvgam` which specifically rely on certain packages. The most important of these is the generation of data necessary to estimate smoothing splines and Gaussian Processes, which rely on the `mgcv`, `brms` and `splines2` packages. The `rstan` and `cmdstanr` packages together with `Rcpp` makes `Stan` conveniently accessible in `R`. If you use some of these features, please also consider citing the related packages.
## Other resources
A number of case studies and step-by-step webinars have been compiled to highlight how GAMs and DGAMs can be useful for analysing multivariate data:
* [Time series in R and Stan using the `mvgam` package](https://www.youtube.com/playlist?list=PLzFHNoUxkCvsFIg6zqogylUfPpaxau_a3){target="_blank"}
* [Ecological Forecasting with Dynamic Generalized Additive Models](https://www.youtube.com/watch?v=0zZopLlomsQ){target="_blank"}
* [State-Space Vector Autoregressions in `mvgam`](https://ecogambler.netlify.app/blog/vector-autoregressions/){target="_blank"}
* [How to interpret and report nonlinear effects from Generalized Additive Models](https://ecogambler.netlify.app/blog/interpreting-gams/){target="_blank"}
* [Phylogenetic smoothing using mgcv](https://ecogambler.netlify.app/blog/phylogenetic-smooths-mgcv/){target="_blank"}
* [Distributed lags (and hierarchical distributed lags) using mgcv and mvgam](https://ecogambler.netlify.app/blog/distributed-lags-mgcv/){target="_blank"}
* [Incorporating time-varying seasonality in forecast models](https://ecogambler.netlify.app/blog/time-varying-seasonality/){target="_blank"}
## Getting help
If you encounter a clear bug, please file an issue with a minimal reproducible example on [GitHub](https://github.com/nicholasjclark/mvgam/issues). Please also feel free to use the [`mvgam` Discussion Board](https://github.com/nicholasjclark/mvgam/discussions) to hunt for or post other discussion topics related to the package, and do check out the [`mvgam` changelog](https://nicholasjclark.github.io/mvgam/news/index.html) for any updates about recent upgrades that the package has incorporated.
## Interested in contributing?
I'm actively seeking PhD students and other researchers to work in the areas of ecological forecasting, multivariate model evaluation and development of `mvgam`. Please reach out if you are interested (n.clark'at'uq.edu.au). Other contributions are also very welcome, but please see [The Contributor Instructions](https://github.com/nicholasjclark/mvgam/blob/master/.github/CONTRIBUTING.md) for general guidelines. Note that
by participating in this project you agree to abide by the terms of its [Contributor Code of Conduct](https://dplyr.tidyverse.org/CODE_OF_CONDUCT).