tsnet/README.Rmd at main · bsiepe/tsnet · GitHub

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---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# tsnet

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The goal of `tsnet` is to include helpful functions for dynamic network modelling in psychology and surrounding fields. The package contains functionality to estimate Bayesian GVAR models in Stan, as well as a test for network comparison. Additionally, the package includes functions to plot posterior estimates and centrality indices. More information is provided in the associated paper [Siepe et al. (2024)](https://doi.org/10.1037/met0000672) (Preprint available [here](https://osf.io/preprints/psyarxiv/uwfjc/)).

## Installation

You can install the released version of `tsnet` from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("tsnet")
```

You can install the development version of `tsnet` from [GitHub](https://github.com/bsiepe/tsnet) with:

``` r
# install.packages("devtools")
devtools::install_github("bsiepe/tsnet")
```

The installation may take some time as the models are compiled upon installation.

## Getting Started

### Estimating Network Models with Stan

The package includes the `stan_gvar` function that can be used to estimate a GVAR model with Stan. We use `rstan` as a backend. More details are included in the package documentation and the associated preprint.

```{r example-fit, eval = FALSE}
library(tsnet)

# Load example data
data(ts_data)

# use data of first individual
data <- subset(ts_data, id == "ID1")

# Estimate network
fit_stan <- stan_gvar(data[,-7],
                 cov_prior = "IW",
                 iter_warmup = 500,
                 iter_sampling = 500,
                 n_chains = 4)

# print summary
print(fit_stan)

```


### Comparing Network Models

This is an example of how to use the package to compare two network models. We here use `BGGM` to estimate the networks, but the `stan_gvar` function can be used as well.

```{r example-test, eval = FALSE}
library(tsnet)

# Load simulated time series data of two individuals
data(ts_data)
data_1 <- subset(ts_data, id == "ID1")
data_2 <- subset(ts_data, id == "ID2")

# Estimate networks
# (should perform detrending etc. in a real use case)
net_1 <- stan_gvar(data_1[,-7],
                   iter_sampling = 1000,
                   n_chains = 4)
net_2 <- stan_gvar(data_2[,-7],
                   iter_sampling = 1000,
                   n_chains = 4)

# Plot individual temporal network estimates
post_plot_1 <- posterior_plot(net_1)
```

![](man/figures/post_plot_example_tsnet.png)


You can then compare these networks, summarize the results and plot the test results. In this case, the test is significant for both the temporal and the contemporaneous network.
```{r compare, eval = FALSE}
# Compare networks
compare_13 <- compare_gvar(net_1,
                           net_2,
                           return_all = TRUE,
                           n_draws = 1000)

# Print summary of results
print(compare_13)

# Plot test results
test_plot_13 <- plot(compare_13,
                     name_a = "Model A",
                     name_b = "Model B")
```

![](man/figures/test_plot_example.png)

## References
If you use the package, please cite the paper that introduces the package and the test:

Siepe, B. S., Kloft, M., & Heck, D. W. (2024). Bayesian estimation and comparison of idiographic network models. *Psychological Methods*. Advance online publication. https://doi.org/10.1037/met0000672

As a BiBTeX entry:

```BiBTeX
@article{siepe2024bayesian,
  title={Bayesian estimation and comparison of idiographic network models},
  author={Siepe, Björn S. and Kloft, Matthias and Heck, Daniel W.},
  journal={Psychological Methods},
  issue={Advance online publication},
  year={2024},
  doi={10.1037/met0000672}
}
```