https://github.com/spatstat/spatstat.explore
Sub-package of spatstat providing functions for exploratory and nonparametric data analysis
https://github.com/spatstat/spatstat.explore
cluster-detection confidence-intervals hypothesis-testing k-function roc-curves scan-statistics significance-testing simulation-envelopes spatial-analysis spatial-data-analysis spatial-sharpening spatial-smoothing spatial-statistics
Last synced: 4 months ago
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Sub-package of spatstat providing functions for exploratory and nonparametric data analysis
- Host: GitHub
- URL: https://github.com/spatstat/spatstat.explore
- Owner: spatstat
- Created: 2022-05-23T05:36:26.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-10-13T06:46:23.000Z (4 months ago)
- Last Synced: 2025-10-21T20:55:11.215Z (4 months ago)
- Topics: cluster-detection, confidence-intervals, hypothesis-testing, k-function, roc-curves, scan-statistics, significance-testing, simulation-envelopes, spatial-analysis, spatial-data-analysis, spatial-sharpening, spatial-smoothing, spatial-statistics
- Language: R
- Homepage:
- Size: 1.9 MB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
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README
# spatstat.explore
## Exploratory/nonparametric data analysis for the spatstat family
[](https://CRAN.R-project.org/package=spatstat.explore)
[](https://github.com/spatstat/spatstat.explore)
The original `spatstat` package has been split into
several sub-packages (See [spatstat/spatstat](https://github.com/spatstat/spatstat))
This package `spatstat.explore` is one of the
sub-packages. It contains the main user-level functions that perform
**exploratory** and **nonparametric** statistical analysis of spatial data,
with the exception of data on linear networks.
Most of the functionality is for spatial point patterns in two dimensions.
There is a very modest amount of functionality for 3D and higher dimensional patterns
and space-time patterns.
### Overview
`spatstat.explore` supports
- data manipulation and exploratory graphics
- exploratory analysis
- smoothing
- cluster detection
- nonparametric estimation
- hypothesis tests (simulation-based and nonparametric)
### Detailed contents
For a full list of functions, see the help file for `spatstat.explore-package`.
#### Exploratory analysis
- Clark-Evans index, Hopkins-Skellam index
- quadrat counting estimates of intensity, quadrat counting test
- Fry plot
- Morisita plot
- scan statistic
- cluster detection (Allard-Fraley cluster set, Byers-Raftery cleaning)
#### Nonparametric estimation of trend
- kernel estimation of intensity of a point pattern
- kernel smoothing of mark values attached to point locations
- kernel estimation of relative risk
- kernel smoothing of a line segment pattern
- bandwidth selection
- spatial CDF
- nonparametric estimation of intensity as a function of a covariate
- ROC curve, AUC
- Sufficient Data Reduction
- optimal thresholding of a covariate
#### Nonparametric estimation of dependence between points
- summary functions (K-function, pair correlation function,
empty space function, nearest neighbour distance function, J-function, etc)
and multi-type versions of these functions
- mark correlation function, mark independence diagnostic
- local summary functions (LISA)
- simulation envelopes of summary functions
- manipulation of summary functions (plot, evaluate, differentiate, smooth etc)
#### Formal inference
- spatial bootstrap
- asymptotic variance estimates
- hypothesis tests (quadrat test, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, envelope tests, Dao-Genton test, balanced independent two-stage test)
#### Data manipulation
- image blurring
- Choi-Hall data sharpening of point locations
- transects of an image along a line or curve
- programming tools