@@ -468,20 +459,17 @@ families. Currently, the package can handle data for the following:
trials
- `nmix()` for count data with imperfect detection (unknown number of
trials)
-- `tweedie()` for overdispersed count data
-Note that only `poisson()`, `nb()`, and `tweedie()` are available if
-using `JAGS`. All families, apart from `tweedie()`, are supported if
-using `Stan`. 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:
+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
set.seed(100)
data <- sim_mvgam(family = betar(),
T = 80,
- trend_model = 'GP',
+ trend_model = GP(),
prop_trend = 0.5,
seasonality = 'shared')
plot_mvgam_series(data = data$data_train, series = 'all')
@@ -492,7 +480,7 @@ plot_mvgam_series(data = data$data_train, series = 'all')
``` r
mod <- mvgam(y ~ s(season, bs = 'cc', k = 7) +
s(season, by = series, m = 1, k = 5),
- trend_model = 'GP',
+ trend_model = GP(),
data = data$data_train,
newdata = data$data_test,
family = betar())
@@ -516,7 +504,7 @@ summary(mod, include_betas = FALSE)
#> logit
#>
#> Trend model:
-#> GP
+#> GP()
#>
#> N series:
#> 3
@@ -566,7 +554,7 @@ summary(mod, include_betas = FALSE)
#> 0 of 2000 iterations saturated the maximum tree depth of 12 (0%)
#> E-FMI indicated no pathological behavior
#>
-#> Samples were drawn using NUTS(diag_e) at Thu Sep 19 11:38:16 AM 2024.
+#> Samples were drawn using NUTS(diag_e) at Mon Sep 30 3:07:18 PM 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split MCMC chains
#> (at convergence, Rhat = 1)
@@ -584,20 +572,16 @@ for(i in 1:3){
There are many more extended uses of `mvgam`, including the ability to
-fit hierarchical GAMs that include dynamic coefficient models, dynamic
-factor and Vector Autoregressive processes. See the
+fit hierarchical State-Space GAMs that include dynamic coefficient
+models, dynamic factors and Vector Autoregressive processes. See the
package documentation for more details. The package
can also be used to generate all necessary data structures, initial
-value functions and modelling code necessary to fit DGAMs using `Stan`
-or `JAGS`. This can be helpful if users wish to make changes to the
-model to better suit their own bespoke research / analysis goals. The
-following resources can be helpful to troubleshoot:
-
-- Stan
- Discourse
-- JAGS Discourse
+value functions and modelling code necessary to fit DGAMs using `Stan`.
+This can be helpful if users wish to make changes to the model to better
+suit their own bespoke research / analysis goals. The
+Stan
+Discourse is a helpful place to troubleshoot.
## License
diff --git a/docs/authors.html b/docs/authors.html
index f7444199..b8909014 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -68,7 +68,7 @@ Authors
Citation
Source: inst/CITATION
Clark & Wells (2021). Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series. Methods in Ecology and Evolution, 14, 771-784. doi.org/10.1111/2041-210X.13974
+Clark & Wells (2022). Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series. Methods in Ecology and Evolution, 14, 771-784. doi.org/10.1111/2041-210X.13974
@Article{,
title = {Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series},
author = {Nicholas J Clark and Konstans Wells},
diff --git a/docs/index.html b/docs/index.html
index a7c17548..6bd2d00d 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -78,12 +78,12 @@
mvgam
MultiVariate (Dynamic) Generalized Addivite Models
-The goal of mvgam is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. The package provides an interface to fit Bayesian DGAMs using either JAGS or Stan as the backend, but note that users are strongly encouraged to opt for Stan over JAGS. The formula syntax is based on that of the package mgcv to provide a familiar GAM modelling interface. The motivation for the package and some of its primary objectives are described in detail by Clark & Wells 2022 (published in Methods in Ecology and Evolution). An introduction to the package and some worked examples are shown in this seminar: Ecological Forecasting with Dynamic Generalized Additive Models.
+The goal of mvgam is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. The package provides an interface to fit Bayesian DGAMs using Stan as the backend. The formula syntax is based on that of the package mgcv to provide a familiar GAM modelling interface. The motivation for the package and some of its primary objectives are described in detail by Clark & Wells 2022 (published in Methods in Ecology and Evolution). An introduction to the package and some worked examples are shown in this seminar: Ecological Forecasting with Dynamic Generalized Additive Models.
Installation
-Install the stable version from CRAN using: install.packages('mvgam'), or install the development version from GitHub using: devtools::install_github("nicholasjclark/mvgam"). Note that to actually condition models with MCMC sampling, either the JAGS software must be installed (along with the R packages rjags and runjags) or the Stan software must be installed (along with either rstan and/or cmdstanr). Only rstan is listed as a dependency of mvgam to ensure that installation is less difficult. If users wish to fit the models using mvgam, please refer to installation links for JAGS here, for Stan with rstan here, or for Stan with cmdstandr here. You will need a fairly recent version of Stan (preferably 2.29 or above) to ensure all the model syntax is recognized. We highly recommend you use Cmdstan through the cmdstanr interface as the backend. This is because Cmdstan is easier to install, is more up to date with new features, and uses less memory than Rstan. See this documentation from the Cmdstan team for more information.
+Install the stable version from CRAN using: install.packages('mvgam'), or install the development version from GitHub using: devtools::install_github("nicholasjclark/mvgam"). Note that to actually condition models with MCMC sampling, the Stan software must be installed (along with either rstan and/or cmdstanr). Only rstan is listed as a dependency of mvgam to ensure that installation is less difficult. If users wish to fit the models using mvgam, please refer to installation links for Stan with rstan here, or for Stan with cmdstandr here. You will need a fairly recent version of Stan (preferably 2.29 or above) to ensure all the model syntax is recognized. We highly recommend you use Cmdstan through the cmdstanr interface as the backend. This is because Cmdstan is easier to install, is more up to date with new features, and uses less memory than Rstan. See this documentation from the Cmdstan team for more information.
Introductory seminar
@@ -110,14 +110,14 @@ Getting started
student_t() for heavy-tailed real-valued data
-lognormal() for non-negative 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
+bernoulli() for binary data
poisson() for count data
@@ -125,13 +125,11 @@ Getting started
binomial() for count data with known number of trials
-beta_binomial() for overdispersed 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)
-
-tweedie() for overdispersed count data
-Note that only poisson(), nb(), and tweedie() are available if using JAGS. All families, apart from tweedie(), are supported if using Stan. 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:
+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:
data <- sim_mvgam(family = betar(),
T = 80,
@@ -145,8 +143,8 @@ Getting startedFit a DGAM 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
-mod <- mvgam(y ~ s(season, bs = 'cc', k = 7) +
- s(season, by = series, m = 1, k = 5),
+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,
@@ -172,16 +170,13 @@ Other resourcesA number of case studies have been compiled to highlight how GAMs and DGAMs can be useful for working with time series data:
- Ecological Forecasting with Dynamic Generalized Additive Models
+- State-Space Vector Autoregressions in
mvgam
- How to interpret and report nonlinear effects from Generalized Additive Models
- Phylogenetic smoothing using mgcv
- Distributed lags (and hierarchical distributed lags) using mgcv and mvgam
- Incorporating time-varying seasonality in forecast models
-The package can also be used to generate all necessary data structures, initial value functions and modelling code necessary to fit DGAMs using Stan or JAGS. This can be helpful if users wish to make changes to the model to better suit their own bespoke research / analysis goals. The following resources can be helpful to troubleshoot:
-
+The package can also be used to generate all necessary data structures, initial value functions and modelling code necessary to fit DGAMs using Stan. This can be helpful if users wish to make changes to the model to better suit their own bespoke research / analysis goals.
Interested in contributing?
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diff --git a/index.Rmd b/index.Rmd
index a6950013..b65ae73c 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -10,11 +10,11 @@ always_allow_html: true
## mvgam
**M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
-The goal of `mvgam` is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. The package provides an interface to fit Bayesian DGAMs using either `JAGS` or `Stan` as the backend, but note that users are strongly encouraged to opt for `Stan` over `JAGS`. The formula syntax is based on that of the package `mgcv` to provide a familiar GAM modelling interface. The motivation for the package and some of its primary objectives are described in detail by [Clark & Wells 2022](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974) (published in *Methods in Ecology and Evolution*). An introduction to the package and some worked examples are shown in this seminar: [Ecological Forecasting with Dynamic Generalized Additive Models](https://www.youtube.com/watch?v=0zZopLlomsQ){target="_blank"}.
+The goal of `mvgam` is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. The package provides an interface to fit Bayesian DGAMs using `Stan` as the backend. The formula syntax is based on that of the package `mgcv` to provide a familiar GAM modelling interface. The motivation for the package and some of its primary objectives are described in detail by [Clark & Wells 2022](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974) (published in *Methods in Ecology and Evolution*). An introduction to the package and some worked examples are shown in this seminar: [Ecological Forecasting with Dynamic Generalized Additive Models](https://www.youtube.com/watch?v=0zZopLlomsQ){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")`. Note that to actually condition models with MCMC sampling, either the `JAGS` software must be installed (along with the `R` packages `rjags` and `runjags`) or the `Stan` software must be installed (along with either `rstan` and/or `cmdstanr`). Only `rstan` is listed as a dependency of `mvgam` to ensure that installation is less difficult. If users wish to fit the models using `mvgam`, please refer to installation links for `JAGS` [here](https://sourceforge.net/projects/mcmc-jags/files/), for `Stan` with `rstan` [here](https://mc-stan.org/users/interfaces/rstan), or for `Stan` with `cmdstandr` [here](https://mc-stan.org/cmdstanr/). You will need a fairly recent version of `Stan` (preferably 2.29 or above) to ensure all the model syntax is recognized. We highly recommend you use `Cmdstan` through the `cmdstanr` interface as the backend. This is because `Cmdstan` is easier to install, is more up to date with new features, and uses less memory than `Rstan`. See [this documentation from the `Cmdstan` team for more information](http://mc-stan.org/cmdstanr/articles/cmdstanr.html#comparison-with-rstan).
+`devtools::install_github("nicholasjclark/mvgam")`. Note that to actually condition models with MCMC sampling, the `Stan` software must be installed (along with either `rstan` and/or `cmdstanr`). Only `rstan` is listed as a dependency of `mvgam` to ensure that installation is less difficult. If users wish to fit the models using `mvgam`, please refer to installation links for `Stan` with `rstan` [here](https://mc-stan.org/users/interfaces/rstan), or for `Stan` with `cmdstandr` [here](https://mc-stan.org/cmdstanr/). You will need a fairly recent version of `Stan` (preferably 2.29 or above) to ensure all the model syntax is recognized. We highly recommend you use `Cmdstan` through the `cmdstanr` interface as the backend. This is because `Cmdstan` is easier to install, is more up to date with new features, and uses less memory than `Rstan`. See [this documentation from the `Cmdstan` team for more information](http://mc-stan.org/cmdstanr/articles/cmdstanr.html#comparison-with-rstan).
## Introductory seminar
```{r echo=FALSE}
@@ -38,9 +38,8 @@ vembedr::embed_url("https://www.youtube.com/watch?v=0zZopLlomsQ")
* `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)
-* `tweedie()` for overdispersed count data
-Note that only `poisson()`, `nb()`, and `tweedie()` are available if using `JAGS`. All families, apart from `tweedie()`, are supported if using `Stan`. 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:
+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)
```
@@ -88,15 +87,13 @@ You can set `build_vignettes = TRUE` when installing with either `devtools::inst
A number of case studies have been compiled to highlight how GAMs and DGAMs can be useful for working with time series data:
* [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"}
-The package can also be used to generate all necessary data structures, initial value functions and modelling code necessary to fit DGAMs using `Stan` or `JAGS`. This can be helpful if users wish to make changes to the model to better suit their own bespoke research / analysis goals. The following resources can be helpful to troubleshoot:
-
-* [Stan Discourse](https://discourse.mc-stan.org/)
-* [JAGS Discourse](https://sourceforge.net/projects/mcmc-jags/)
+The package can also be used to generate all necessary data structures, initial value functions and modelling code necessary to fit DGAMs using `Stan`. This can be helpful if users wish to make changes to the model to better suit their own bespoke research / analysis goals.
## 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)
diff --git a/index.md b/index.md
index a9fce30b..51e21c8c 100644
--- a/index.md
+++ b/index.md
@@ -10,11 +10,10 @@
The goal of `mvgam` is to use a Bayesian framework to estimate
parameters of Dynamic Generalized Additive Models (DGAMs) for time
series with dynamic trend components. The package provides an interface
-to fit Bayesian DGAMs using either `JAGS` or `Stan` as the backend, but
-note that users are strongly encouraged to opt for `Stan` over `JAGS`.
-The formula syntax is based on that of the package `mgcv` to provide a
-familiar GAM modelling interface. The motivation for the package and
-some of its primary objectives are described in detail by [Clark & Wells
+to fit Bayesian DGAMs using `Stan` as the backend. The formula syntax is
+based on that of the package `mgcv` to provide a familiar GAM modelling
+interface. The motivation for the package and some of its primary
+objectives are described in detail by [Clark & Wells
2022](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974)
(published in *Methods in Ecology and Evolution*). An introduction to
the package and some worked examples are shown in this seminar:
@@ -27,21 +26,19 @@ Models.
Install the stable version from CRAN using: `install.packages('mvgam')`,
or install the development version from `GitHub` using:
`devtools::install_github("nicholasjclark/mvgam")`. Note that to
-actually condition models with MCMC sampling, either the `JAGS` software
-must be installed (along with the `R` packages `rjags` and `runjags`) or
-the `Stan` software must be installed (along with either `rstan` and/or
-`cmdstanr`). Only `rstan` is listed as a dependency of `mvgam` to ensure
-that installation is less difficult. If users wish to fit the models
-using `mvgam`, please refer to installation links for `JAGS`
-[here](https://sourceforge.net/projects/mcmc-jags/files/), for `Stan`
-with `rstan` [here](https://mc-stan.org/users/interfaces/rstan), or for
-`Stan` with `cmdstandr` [here](https://mc-stan.org/cmdstanr/). You will
-need a fairly recent version of `Stan` (preferably 2.29 or above) to
-ensure all the model syntax is recognized. We highly recommend you use
-`Cmdstan` through the `cmdstanr` interface as the backend. This is
-because `Cmdstan` is easier to install, is more up to date with new
-features, and uses less memory than `Rstan`. See [this documentation
-from the `Cmdstan` team for more
+actually condition models with MCMC sampling, the `Stan` software must
+be installed (along with either `rstan` and/or `cmdstanr`). Only `rstan`
+is listed as a dependency of `mvgam` to ensure that installation is less
+difficult. If users wish to fit the models using `mvgam`, please refer
+to installation links for `Stan` with `rstan`
+[here](https://mc-stan.org/users/interfaces/rstan), or for `Stan` with
+`cmdstandr` [here](https://mc-stan.org/cmdstanr/). You will need a
+fairly recent version of `Stan` (preferably 2.29 or above) to ensure all
+the model syntax is recognized. We highly recommend you use `Cmdstan`
+through the `cmdstanr` interface as the backend. This is because
+`Cmdstan` is easier to install, is more up to date with new features,
+and uses less memory than `Rstan`. See [this documentation from the
+`Cmdstan` team for more
information](http://mc-stan.org/cmdstanr/articles/cmdstanr.html#comparison-with-rstan).
## Introductory seminar
@@ -79,14 +76,11 @@ package can handle data for the following families:
trials
- `nmix()` for count data with imperfect detection (unknown number of
trials)
-- `tweedie()` for overdispersed count data
-Note that only `poisson()`, `nb()`, and `tweedie()` are available if
-using `JAGS`. All families, apart from `tweedie()`, are supported if
-using `Stan`. 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:
+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
data <- sim_mvgam(family = betar(),
@@ -161,6 +155,9 @@ DGAMs can be useful for working with time series data:
- Ecological Forecasting with Dynamic Generalized Additive
Models
+- State-Space Vector Autoregressions in
+ mvgam
- How to interpret and report nonlinear effects from
Generalized Additive Models
@@ -175,12 +172,8 @@ DGAMs can be useful for working with time series data:
The package can also be used to generate all necessary data structures,
initial value functions and modelling code necessary to fit DGAMs using
-`Stan` or `JAGS`. This can be helpful if users wish to make changes to
-the model to better suit their own bespoke research / analysis goals.
-The following resources can be helpful to troubleshoot:
-
-- [Stan Discourse](https://discourse.mc-stan.org/)
-- [JAGS Discourse](https://sourceforge.net/projects/mcmc-jags/)
+`Stan`. This can be helpful if users wish to make changes to the model
+to better suit their own bespoke research / analysis goals.
## Interested in contributing?
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