Package index • rgeoda

All functions
`LISA-class` `LISA`	LISA class (Internally Used)
`Weight-class` `Weight`	Weight class (Internally Used)
`as.data.frame(<geoda>)`	convert rgeoda instance to data.frame
`as.geoda()`	Create an instance of geoda-class from either an 'sf' or 'sp' object
`as.matrix(<Weight>)`	spatial weights to matrix
`azp_greedy()`	A greedy algorithm to solve the AZP problem
`azp_sa()`	A simulated annealing algorithm to solve the AZP problem
`azp_tabu()`	A tabu algorithm to solve the AZP problem
`create_weights()`	Create an empty weights
`distance_weights()`	Distance-based Spatial Weights
`eb_rates()`	Empirical Bayes(EB) Rate
`gda_distance_weights()`	(For internally use and test only) Distance-based Spatial Weights
`gda_kernel_knn_weights()`	(For internally use and test only) K-NN Kernel Spatial Weights
`gda_kernel_weights()`	(For internally use and test only) Distance-based Kernel Spatial Weights
`gda_knn_weights()`	(For internally use and test only) K-Nearest Neighbors-based Spatial Weights
`gda_min_distthreshold()`	(For internally use and test only) Minimum Distance Threshold for Distance-based Weights
`gda_queen_weights()`	(For internally use and test only) Queen Contiguity Spatial Weights
`gda_rook_weights()`	(For internally use and test only) Rook Contiguity Spatial Weights
`geoda-class` `geoda`	'geoda' class
`geoda_open()`	Create an instance of geoda-class by reading from an ESRI Shapefile dataset
`get_neighbors()`	Neighbors of one observation
`get_neighbors_weights()`	Weights values of the neighbors of one observation
`has_isolates()`	Isolation/Island in Spatial Weights
`hinge15_breaks()`	(Box) Hinge15 Breaks
`hinge30_breaks()`	(Box) Hinge30 Breaks
`is_symmetric()`	Symmetry of Weights Matrix
`join_count_ratio()`	Join Count Ratio
`kernel_knn_weights()`	K-NN Kernel Spatial Weights
`kernel_weights()`	Distance-based Kernel Spatial Weights
`knn_weights()`	K-Nearest Neighbors-based Spatial Weights
`lisa_bo()`	Bonferroni bound value of local spatial autocorrelation
`lisa_clusters()`	Get local cluster indicators
`lisa_colors()`	Get cluster colors
`lisa_fdr()`	False Discovery Rate value of local spatial autocorrelation
`lisa_labels()`	Get cluster labels
`lisa_num_nbrs()`	Get numbers of neighbors for all observations
`lisa_pvalues()`	Get pseudo-p values of LISA
`lisa_values()`	Get LISA values
`local_bijoincount()`	Bivariate Local Join Count Statistics
`local_bimoran()`	Bivariate Local Moran Statistics
`local_g()`	Local Getis-Ord's G Statistics
`local_geary()`	Local Geary Statistics
`local_gstar()`	Local Getis-Ord's G* Statistics
`local_joincount()`	Local Join Count Statistics
`local_moran()`	Local Moran Statistics
`local_moran_eb()`	Local Moran with Empirical Bayes(EB) Rate
`local_multigeary()`	Local Multivariate Geary Statistics
`local_multijoincount()`	(Multivariate) Colocation Local Join Count Statistics
`local_multiquantilelisa()`	Multivariate Quantile LISA Statistics
`local_quantilelisa()`	Quantile LISA Statistics
`make_spatial()`	Make Spatial
`max_neighbors()`	Maximum Neighbors of Spatial Weights
`maxp_greedy()`	A greedy algorithm to solve the max-p-region problem
`maxp_sa()`	A simulated annealing algorithm to solve the max-p-region problem
`maxp_tabu()`	A tabu-search algorithm to solve the max-p-region problem
`mean_neighbors()`	Mean Neighbors of Spatial Weights
`median_neighbors()`	Median Neighbors of Spatial Weights
`min_distthreshold()`	Minimum Distance Threshold for Distance-based Weights
`min_neighbors()`	Minimum Neighbors of Spatial Weights
`natural_breaks()`	Natural Breaks (Jenks)
`neighbor_match_test()`	Local Neighbor Match Test
`p_GeoDa-class` `p_GeoDa` `$,p_GeoDa-method`	p_GeoDa
`p_GeoDaTable-class` `p_GeoDaTable` `$,p_GeoDaTable-method`	p_GeoDaTable
`p_GeoDaWeight-class` `p_GeoDaWeight` `$,p_GeoDaWeight-method`	p_GeoDaWeight
`p_LISA-class` `p_LISA` `$,p_LISA-method`	p_LISA
`percentile_breaks()`	Percentile Breaks
`quantile_breaks()`	Quantile Breaks
`queen_weights()`	Queen Contiguity Spatial Weights
`read_gal()`	Read a .GAL file
`read_gwt()`	Read a .GWT file
`read_swm()`	Read a .SWM file
`redcap()`	Regionalization with dynamically constrained agglomerative clustering and partitioning
`rook_weights()`	Rook Contiguity Spatial Weights
`save_weights()`	Save Spatial Weights
`schc()`	Spatially Constrained Hierarchical Clucstering (SCHC)
`set_neighbors()`	Set neighbors of an observation
`set_neighbors_with_weights()`	Set neighbors and weights values of an observation
`sf_to_geoda()`	Create an instance of geoda-class from a 'sf' object
`skater()`	Spatial C(K)luster Analysis by Tree Edge Removal
`sp_to_geoda()`	Create an instance of geoda-class from a 'sp' object
`spatial_lag()`	Spatial Lag
`spatial_validation()`	Spatial Validation
`stddev_breaks()`	Standard Deviation Breaks
`summary(<Weight>)`	Summary of Spatial Weights
`update_weights()`	Update meta data of a spatial weights
`weights_sparsity()`	Sparsity of Spatial Weights

Reference

All functions