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sdsfun

Spatial Data Science Complementary Features

Installation

  • Install from CRAN with:
install.packages("sdsfun")
  • Install development binary version from R-universe with:
install.packages('sdsfun',
                 repos = c("https://stscl.r-universe.dev",
                           "https://cloud.r-project.org"),
                 dep = TRUE)
  • Install development source version from GitHub with:
# install.packages("devtools")
devtools::install_github("stscl/sdsfun", dep = TRUE)

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Version

Install

install.packages('sdsfun')

Monthly Downloads

555

Version

0.8.0

License

GPL-3

Issues

Pull Requests

Stars

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Maintainer

Wenbo Lv

Last Published

May 12th, 2025

Functions in sdsfun (0.8.0)

spdep_contiguity_swm

constructs spatial weight matrices based on contiguity
spdep_lmtest

spatial linear models selection
spvar

spatial variance
standardize_vector

standardization
spdep_distance_swm

constructs spatial weight matrices based on distance
ssh_test

spatial stratified heterogeneity test
%>%

Pipe operator
rm_lineartrend

remove variable linear trend based on covariate
moran_test

global spatial autocorrelation test
spade_psd

only spade power of spatial determinant
normalize_vector

normalization
sf_voronoi_diagram

generates voronoi diagram
spdep_nb

construct neighbours list
tbl_all2int

convert discrete variables in a tibble to integers
tbl_xyz2mat

convert xyz tbl to matrix
formula_varname

get variable names in a formula and data
dummy_vec

transforming a categorical variable into dummy variables
generate_subsets

generate subsets of a set
fuzzyoverlay

spatial fuzzy overlay
discretize_vector

discretization
dummy_tbl

transforming a category tibble into the corresponding dummy variable tibble
sf_geometry_name

sf object geometry column name
sf_geometry_type

sf object geometry type
check_tbl_na

check for NA values in a tibble
sf_gk_proj_cgcs2000

generates cgcs2000 Gauss-Kruger projection epsg coding character
sf_utm_proj_wgs84

generates wgs84 utm projection epsg coding character
cor_test

(partial) correlation test
inverse_distance_swm

construct inverse distance weight
loess_optnum

determine optimal spatial data discretization for individual variables
spdep_skater

spatial c(k)luster analysis by tree edge removal
geodetector_q

only geodetector q-value
sf_coordinates

extract locations
hclustgeo_disc

hierarchical clustering with spatial soft constraints
sf_distance_matrix

generates distance matrix