# Getting started with the tna package

This vignette showcases some basic usage of the `tna` package. First we load the package that we will use for this example.

    library("tna")
    library("tibble")
    library("dplyr")
    library("gt")

We also load the `group_regulation` data available in the package (see `?group_regulation` for further information)

    data("group_regulation", package = "tna")

We build a TNA model using this data with the `tna()` function .

    tna_model <- tna(group_regulation)

To visualize the model, we can use the standard `plot()` function.

    plot(tna_model, cut = 0.2, minimum = 0.05,
         edge.label.position= 0.8, edge.label.cex = 0.7)

The initial state probabilities are

    data.frame(`Initial prob.` = tna_model$inits, check.names = FALSE) |>
      rownames_to_column("Action") |>
      arrange(desc(`Initial prob.`)) |>
      gt() |>
      fmt_percent()

Action | Initial prob.
--- | ---
consensus | 21.40%
plan | 20.45%
discuss | 17.55%
emotion | 15.15%
monitor | 14.40%
cohesion | 6.05%
synthesis | 1.95%
coregulate | 1.90%
adapt | 1.15%

and the transitions probabilities are

    tna_model$weights |>
      data.frame() |>
      rownames_to_column("From\\To") |>
      gt() |>
      fmt_percent()

From\To | adapt | cohesion | consensus | coregulate | discuss | emotion | monitor | plan | synthesis
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
adapt | 0.00% | 27.31% | 47.74% | 2.16% | 5.89% | 11.98% | 3.34% | 1.57% | 0.00%
cohesion | 0.29% | 2.71% | 49.79% | 11.92% | 5.96% | 11.56% | 3.30% | 14.10% | 0.35%
consensus | 0.47% | 1.49% | 8.20% | 18.77% | 18.80% | 7.27% | 4.66% | 39.58% | 0.76%
coregulate | 1.62% | 3.60% | 13.45% | 2.34% | 27.36% | 17.21% | 8.63% | 23.91% | 1.88%
discuss | 7.14% | 4.76% | 32.12% | 8.43% | 19.49% | 10.58% | 2.23% | 1.16% | 14.10%
emotion | 0.25% | 32.53% | 32.04% | 3.42% | 10.19% | 7.68% | 3.63% | 9.98% | 0.28%
monitor | 1.12% | 5.58% | 15.91% | 5.79% | 37.54% | 9.07% | 1.81% | 21.56% | 1.61%
plan | 0.10% | 2.52% | 29.04% | 1.72% | 6.79% | 14.68% | 7.55% | 37.42% | 0.18%
synthesis | 23.47% | 3.37% | 46.63% | 4.45% | 6.29% | 7.06% | 1.23% | 7.52% | 0.00%

The function `centralities()` can be used to compute various centrality measures (see `?centralities` for more information). These measures can also be visualized with the `plot()` function.

    centrality_measures <- c("BetweennessRSP", "Closeness", "InStrength", "OutStrength")
    cents_withoutloops <- centralities(
      tna_model,
      measures = centrality_measures,
      loops = FALSE,
      normalize = TRUE
    )
    plot(cents_withoutloops, ncol = 2, model = tna_model)