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It contains the main user-level functions that perform\n**exploratory** and **nonparametric** statistical analysis of spatial data,\nwith the exception of data on linear networks.\n\nMost of the functionality is for spatial point patterns in two dimensions.\nThere is a very modest amount of functionality for 3D and higher dimensional patterns\nand space-time patterns.\n\n### Overview \n\n`spatstat.explore` supports\n\n- data manipulation and exploratory graphics\n- exploratory analysis \n- smoothing\n- cluster detection\n- nonparametric estimation \n- hypothesis tests (simulation-based and nonparametric)\n\n### Detailed contents\n\nFor a full list of functions, see the help file for `spatstat.explore-package`.\n\n#### Exploratory analysis \n\n- Clark-Evans index, Hopkins-Skellam index\n- quadrat counting estimates of intensity, quadrat counting test\n- Fry plot\n- Morisita plot\n- scan statistic\n- cluster detection (Allard-Fraley cluster set, Byers-Raftery cleaning)\n\n#### Nonparametric estimation of trend\n\n- kernel estimation of intensity of a point pattern\n- kernel smoothing of mark values attached to point locations\n- kernel estimation of relative risk\n- kernel smoothing of a line segment pattern\n- bandwidth selection\n- spatial CDF \n- nonparametric estimation of intensity as a function of a covariate\n- ROC curve, AUC\n- Sufficient Data Reduction\n- optimal thresholding of a covariate\n\n#### Nonparametric estimation of dependence between points\n\n- summary functions (K-function, pair correlation function,\nempty space function, nearest neighbour distance function, J-function, etc)\nand multi-type versions of these functions\n- mark correlation function, mark independence diagnostic\n- local summary functions (LISA)\n- simulation envelopes of summary functions\n- manipulation of summary functions (plot, evaluate, differentiate, smooth etc)\n\n#### Formal inference\n\n- spatial bootstrap\n- asymptotic variance estimates\n- 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)\n\n#### Data manipulation\n\n- image blurring\n- Choi-Hall data sharpening of point locations\n- transects of an image along a line or curve\n- programming tools\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspatstat%2Fspatstat.explore","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fspatstat%2Fspatstat.explore","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspatstat%2Fspatstat.explore/lists"}