Package index • netdiffuseR

All functions
`approx_geodesic()` `approx_geodist()`	Approximate Geodesic Distances
`as.array(<diffnet>)`	Coerce a diffnet graph into an array
`as_dgCMatrix()` `as.dgCMatrix()` `as_spmat()`	Coerce a matrix-like objects to `dgCMatrix` (sparse matrix)
`fitbass()` `plot(<diffnet_bass>)` `bass_F()` `bass_dF()` `bass_f()`	Bass Model
`resample_graph()` `bootnet()` `c(<diffnet_bootnet>)` `print(<diffnet_bootnet>)` `hist(<diffnet_bootnet>)` `plot(<diffnet_bootnet>)`	Network Bootstrapping
`brfarmers`	Brazilian Farmers
`brfarmersDiffNet`	`diffnet` version of the Brazilian Farmers data
`c(<diffnet>)`	Combine diffnet objects
`classify_adopters()` `classify()` `ftable(<diffnet_adopters>)` `as.data.frame(<diffnet_adopters>)` `plot(<diffnet_adopters>)`	Classify adopters accordingly to Time of Adoption and Threshold levels.
`classify_graph()`	Analyze an R object to identify the class of graph (if any)
`collapse_timeframes()`	Collapse Timeframes in a Longitudinal Edgelist
`cumulative_adopt_count()`	Cummulative count of adopters
`degree_adoption_diagnostic()`	Degree and Time of Adoption Diagnostic
`dgr()` `plot(<diffnet_degSeq>)`	Indegree, outdegree and degree of the vertices
`diag_expand()`	Creates a square matrix suitable for spatial statistics models.
`^`(<diffnet>) `graph_power()` `/`(<diffnet>) `-`(<diffnet>) `*`(<diffnet>) `&`(<diffnet>) `\|`(<diffnet>)	`diffnet` Arithmetic and Logical Operators
`as_diffnet()` `new_diffnet()` `as.data.frame(<diffnet>)` `diffnet.attrs()` `diffnet.attrs<-`() `diffnet.toa()` `diffnet.toa<-`() `print(<diffnet>)` `nodes()` `diffnetLapply()` `str(<diffnet>)` `dimnames(<diffnet>)` `t(<diffnet>)` `dim(<diffnet>)` `is_undirected()` `is_self()` `is_multiple()` `is_valued()`	Creates a `diffnet` class object
`diffnet_check_attr_class()`	Infer whether `value` is dynamic or static.
`[[`(<diffnet>) `[[<-`(<diffnet>) `[`(<diffnet>) `[<-`(<diffnet>)	Indexing diffnet objects (on development)
`%*%`	Matrix multiplication
`diffreg()`	Diffusion regression model
`diffusion-data`	Diffusion Network Datasets
`diffusionMap()` `diffmap()` `image(<diffnet_diffmap>)` `print(<diffnet_diffmap>)` `plot(<diffnet_diffmap>)`	Creates a heatmap based on a graph layout and a vertex attribute
`drawColorKey()`	Draw a color key in the current device
`edgelist_to_adjmat()` `adjmat_to_edgelist()`	Conversion between adjacency matrix and edgelist
`edges_coords()`	Compute ego/alter edge coordinates considering alter's size and aspect ratio
`ego_variance()`	Computes variance of \(Y\) at ego level
`egonet_attrs()`	Retrieve alter's attributes (network effects)
`epigames`	Epi Games Dataset
`epigamesDiffNet`	`diffnet` version of the Epi Games data
`exposure()`	Ego exposure
`fakeDynEdgelist`	Fake dynamic edgelist
`fakeEdgelist`	Fake static edgelist
`fakesurvey`	Fake survey data
`fakesurveyDyn`	Fake longitudinal survey data
`grid_distribution()`	Distribution over a grid
`hazard_rate()` `plot_hazard()` `plot(<diffnet_hr>)`	Network Hazard Rate
`diffnet_to_igraph()` `igraph_to_diffnet()`	Coercion between graph classes
`infection()` `susceptibility()`	Susceptibility and Infection
`isolated()` `drop_isolated()`	Find and remove isolated vertices
`kfamily`	Korean Family Planning
`kfamilyDiffNet`	`diffnet` version of the Korean Family Planning data
`matrix_compare()` `compare_matrix()`	Non-zero element-wise comparison between two sparse matrices
`medInnovations`	Medical Innovation
`medInnovationsDiffNet`	`diffnet` version of the Medical Innovation data
`mentor_matching()` `leader_matching()` `plot(<diffnet_mentor>)`	Optimal Leader/Mentor Matching
`moran()`	Computes Moran's I correlation index
`netdiffuseR-graphs`	Network data formats
`netdiffuseR-options`	netdiffuseR default options
`netdiffuseR` `netdiffuseR-package`	netdiffuseR
`netmatch_prepare()` `netmatch()`	Matching Estimators with Network Data
`diffnet_to_network()` `diffnet_to_networkDynamic()` `networkDynamic_to_diffnet()` `network_to_diffnet()`	Coercion between `diffnet`, `network` and `networkDynamic`
`nvertices()` `nnodes()` `nedges()` `nlinks()` `nslices()`	Count the number of vertices/edges/slices in a graph
`permute_graph()` `rewire_permute()` `rewire_qap()`	Permute the values of a matrix
`plot(<diffnet>)`	S3 plotting method for diffnet objects.
`plot_adopters()`	Visualize adopters and cumulative adopters
`plot_diffnet()`	Plot the diffusion process
`plot_diffnet2()`	Another way of visualizing diffusion
`plot_infectsuscep()`	Plot distribution of infect/suscep
`plot_threshold()`	Threshold levels through time
`pretty_within()`	Pretty numbers within a range.
`rdiffnet_multiple()` `rdiffnet()`	Random diffnet network
`read_pajek()` `read_ml()`	Read foreign graph formats
`read_ucinet_head()` `read_ucinet()`	Reads UCINET files
`recode()`	Recodes an edgelist such that ids go from 1 to n
`rescale_vertex_igraph()` `igraph_vertex_rescale()` `vertex_rescale_igraph()`	Rescale vertex size to be used in `plot.igraph`.
`rewire_graph()`	Graph rewiring algorithms
`rgraph_ba()`	Scale-free and Homophilic Random Networks
`rgraph_er()`	Erdos-Renyi model
`rgraph_ws()`	Watts-Strogatz model
`ring_lattice()`	Ring lattice graph
`round_to_seq()`	Takes a numeric vector and maps it into a finite length sequence
`select_egoalter()` `adopt_changes()` `summary(<diffnet_adoptChanges>)`	Calculate the number of adoption changes between ego and alter.
`split_behaviors()`	Splitting behaviors
`struct_equiv()` `print(<diffnet_se>)`	Structural Equivalence
`n_rewires()` `struct_test()` `c(<diffnet_struct_test>)` `print(<diffnet_struct_test>)` `hist(<diffnet_struct_test>)` `struct_test_asymp()`	Structure dependence test
`summary(<diffnet>)`	Summary of diffnet objects
`survey_to_diffnet()` `edgelist_to_diffnet()`	Convert survey-like data and edgelists to a `diffnet` object
`threshold()`	Retrive threshold levels from the exposure matrix
`toa_diff()`	Difference in Time of Adoption (TOA) between individuals
`toa_mat()`	Time of adoption matrix
`transformGraphBy()`	Apply a function to a graph considering non-diagonal structural zeros
`vertex_covariate_compare()`	Comparisons at dyadic level
`vertex_covariate_dist()` `vertex_mahalanobis_dist()`	Computes covariate distance between connected vertices
`weighted_var()` `wvar()`	Computes weighted variance

Reference

All functions