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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 engagement data available in the package (see ?engagement for further information)

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

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

tna_model <- tna(engagement)

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

plot(tna_model)

The initial state probabilities are

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

and the transitions probabilities are

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

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)

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They may not be fully stable and should be used with caution. We make no claims about them.
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