بنقرة واحدة
tsna
R tsna package for temporal network analysis. Use for analyzing temporal paths and metrics in dynamic networks.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
R tsna package for temporal network analysis. Use for analyzing temporal paths and metrics in dynamic networks.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".
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| name | tsna |
| description | R tsna package for temporal network analysis. Use for analyzing temporal paths and metrics in dynamic networks. |
Tools for temporal social network analysis.
library(tsna)
# Forward reachable set
tPath(dnet, v = 1, direction = "fwd",
start = 0, end = 10)
# Backward reachable set
tPath(dnet, v = 1, direction = "bkwd",
start = 0, end = 10)
# Geodesic distance
tPath(dnet, v = 1, type = "geodesic")
# Forward reachability
reach_fwd <- tReach(dnet, direction = "fwd")
# Backward reachability
reach_bkwd <- tReach(dnet, direction = "bkwd")
# Reachability matrix
tReach(dnet, graph = TRUE)
# Temporal degree
tDegree(dnet)
# At specific time
tDegree(dnet, at = 5)
# Over interval
tDegree(dnet, start = 0, end = 10)
# Apply SNA function over time
tSnaStats(dnet, snafun = "betweenness")
tSnaStats(dnet, snafun = "closeness")
tSnaStats(dnet, snafun = "degree")
# Custom function
tSnaStats(dnet, snafun = function(x) {
mean(degree(x))
})
# Edge durations
edgeDuration(dnet)
# Mean duration
mean(edgeDuration(dnet))
# Vertex activity duration
vertexDuration(dnet)
# Infection/transmission tree
tree <- tPath(dnet, v = 1, direction = "fwd",
type = "earliest.arrive")
# Plot tree
plot(tree)
# Correlation between time slices
tCorrelate(dnet, lag = 1)
# Aggregate over time windows
tErgmStats(dnet,
formula = ~ edges + mutual,
start = 0, end = 10,
time.interval = 1)