Integrate dynamic behavioral attributes into epigamesDiffNet

[aoliveram]

This PR addresses Issue #75. It updates the epigamesDiffNet dataset construction pipeline to move behavioral attributes from static averages to dynamic time-varying attributes (vertex.dyn.attrs).

Changes

• New Python processing logic to parse raw histories.csv logs.
• Updated data-raw/epigamesDiffNet.R to import these dynamic series.
• Inclusion of mask, med, and quarantine as dynamic variables within the epigamesDiffNet object.

By including dynamic attributes, exposure() will now correctly account for whether a node or its neighbors were using masks at the exact time of contact, rather than using a 15-day average.

[aoliveram]

@gvegayon the package now presents a weighted non-cumulative version. The user can construct a simpler network running two lines. This is what the documentation says:

\code{diffnet} version of the Epi Games data

A directed dynamic graph with 594 vertices and 15 time periods. The attributes
in the graph are described in \code{\link{epigames}}.

By default, this \code{diffnet} object is **non-cumulative** (each slice represents 
ephemeral daily contacts) and **valued** (edge weights represent contact duration in seconds). 

To reconstruct the classic cumulative/binarized network, you can run:

\preformatted{
epigames_cumul <- epigamesDiffNet

# 1. Accumulate the history across time periods
epigames_cumul$graph <- Reduce("+", epigames_cumul$graph, accumulate = TRUE)

# 2. Apply a logical cut-off to binarize the network
epigames_cumul$graph <- lapply(epigames_cumul$graph, function(m) { m@x[] <- 1; m })
}

Non-adopters have \code{toa = NA}.