Man pages for tbonne/netTS
netTS: network time series
| check.timescale | Quantify how changing window size alters the resulting time... |
| check.windowsize | Bootstrap convergence check with subsampling |
| check.windowsize.plot | Plot function for windowsize check outputs |
| convergence.check.boot | Bootstrap convergence check |
| convergence.check.boot.graph | Bootstrap convergence check for network level measures |
| convergence.check.value | Convergence check |
| convergence.check.var | Variance by window size check |
| cor_between_graphs | Estimate similarity between graphs using correlation |
| cosine_between_graphs | Estimate consine similarity between graphs |
| cosine_between_nodes | Estimate consine similarity between nodes in a graph |
| create.an.edgeList | Create an edge list |
| create.a.network | Create a network |
| create.a.network.SRI | Create a network from an edge list using the SRI. |
| create.window | Create a window |
| degree_mean | Mean degree of a network |
| df.scans | Data on observational scans of a vervet monkey group |
| dist_between_graphs | Estimate similarity between graphs using Euclidean distance |
| dyad_change | dyad_change function |
| dyad_diff | dyad_diff function |
| dyad_mean | dyad_mean function |
| dyadTS | dyadTS function |
| dyadTS.plot | Plot function for dyadTS outputs |
| dyad_weight | dyad_weight function |
| edge.weight.skewness | Estimate skewness of the edge weight distribution for each... |
| effort.focal | Focal sampling |
| effort.scan | Unique scan IDs |
| effort.time | Min/Max time per day |
| eigen_mean | Mean eigenvector centrality of a network |
| extract_lagged_measure_dyads | Extract dyad level measures from a list of networks when the... |
| extract_lagged_measure_network | Extract measures from a list of networks when the measure... |
| extract_lagged_measure_nodes | Extract node level measures from a list of networks when the... |
| extract_measure_dyads | Extract dyad level network measures from a list of networks |
| extract_measure_network | Extract network measures from a list of networks |
| extract_measure_nodes | Extract node level network measures from a list of networks |
| extract_networks | Extract networks from a moving window |
| extract_networks_para | Extract networks from a moving window using multiple cores |
| graphTS | graphTS function |
| graphTS.plot | Plot function for graphTS outputs |
| groomEvents | Data on grooming events in a vervet group |
| net.para | Extract networks in parallel using a dataframe of times |
| net.window.para | Extract one network within time constriants |
| node_first_last | First and last observation time of each node |
| nodeTS | nodeTS function |
| nodeTS.plot | Plot function for nodeTS outputs |
| order_events | Order events alphabetically |
| perm.edge.degseq | Perform edge permutations maintaining the degree distribution... |
| perm.edge.weights | Perform edge weight permutations |
| perm.events | Perform permutation on the events dataframe |
| perm.events.directed | Perform permutation on the events dataframe, maintaining... |
| permutation.graph.values | Use permutation to extract uncertainty |
| sim.events.data | Simulate some observation data |
| trim_dyads | Trim dyads. |
| trim_nodes | Trim nodes. |
| weighted_mean | Calculate the weighted average correcting for nodes enter or... |
