Mobility Network Analysis
addEffect
alter_covariate
associativity_all_AC_covar_bin
associativity_one_AC_covar_bin
autoCorrelationTest
avoiding_dissimilar_covar_bin
avoiding_dissimilar_covar_cont
concentration_AC_dyad_covar
concentration_AC_resource_covar_bin
concentration_AC
concentration_basic_cube
concentration_basic_squared
concentration_basic
concentration_GW_dyad_covar
concentration_GW_resource_covar_bin
concentration_GW
concentration_norm_squared
concentration_norm
concentration_prop_orig_cov
concentration_prop
concentration_rankGW
createAlgorithm
createEdgelist
createEffects
createEffectsObject
createNetwork
createNodeSet
createNodeVariable
createProcessState
createWeightedCache
crowding_out_prop_covar_bin
dyadic_covariate_resource_attribute
dyadic_covariate
estimateMobilityNetwork
extractTraces
getIndegree
getMultinomialStatistics
getTieWeights
gofMobilityNetwork
in_ties_loops
in_weights_AC
in_weights_exponent
in_weights_GW
joining_similar_avoiding_dissimilar_covar_bin
joining_similar_avoiding_dissimilar_covar_cont
loops_AC
loops_additional_origin
loops_GW
loops_node_covar
loops_resource_covar_node_covar
loops_resource_covar
loops_x_loops_additional_origin
loops
Example Data for the MoNAn Package
MoNAn: Mobility Network Analysis
monanDataCreate
Exemplary Outcome Objects for the MoNAn Package
present_relations
print.effectsList.monan
print.processState.monan
reciprocity_AC_dyad_covar_bin
reciprocity_AC_dyad_covar
reciprocity_AC
reciprocity_basic
reciprocity_GW_dyad_covar_bin
reciprocity_GW_dyad_covar
reciprocity_GW
reciprocity_min_dyad_covar
reciprocity_min_resource_covar
reciprocity_min
resource_covar_to_node_covar
same_covariate
scoreTest
sim_covariate
simulateMobilityNetworks
staying_by_prop_bin_inflow
target_change_match
test_effectTest for each person in the example data and one randomly s...
transitivity_AC
transitivity_basic
transitivity_GW
transitivity_min
transitivity_netflow
triad120C
triad120D
triad120U
Implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. (2022) <doi:10.1016/j.socnet.2021.08.003>. The purpose of the model is to analyse the structure of mobility, incorporating exogenous predictors pertaining to individuals and locations known from classical mobility analyses, as well as modelling emergent mobility patterns akin to structural patterns known from the statistical analysis of social networks.