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  "Date": "2026-06-30",
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  "Title": "Local and Geographically Weighted Spatial Statistics Tools",
  "Authors@R": "person(given = \"Stamatis\",\nfamily = \"Kalogirou\",\nrole = c(\"aut\", \"cre\"),\nemail = \"stamatis.science@gmail.com\")",
  "Description": "Provides researchers and educators with easy-to-learn,\nuser friendly tools for calculating key spatial statistics and\nfor applying simple as well as advanced methods of spatial\nanalysis on real data. These include: Local Pearson and\nGeographically Weighted Pearson Correlation Coefficients;\nSpatial Inequality Measures (Gini coefficient, Spatial Gini,\nLocation Quotient (LQ) and Focal Location Quotient); Spatial\nAutocorrelation indices (Global and Local Moran's I); several\nGeographically Weighted Regression techniques, including the\nGeographically Weighted Zero-Inflated Poisson Regression; tools\nfor computing variables used in Spatial Interaction Models; and\nother spatial analysis tools (other geographically weighted\nstatistics). The local correlation tools were originally\ndeveloped to test for local multicollinearity among the\nexplanatory variables of local regression models and can also\nbe used to examine the local association between pairs of\nvariables. The package also contains functions for measuring\nthe significance of each statistic calculated, mainly based on\nMonte Carlo simulations, and comes with two example datasets,\none of which is a spatial data frame referring to the\nmunicipalities of Greece. Methods are described in Kalogirou\n(2012) <doi:10.1007/s10037-011-0061-y>, Kalogirou (2016)\n<doi:10.1111/gean.12092>, and Rey and Smith (2013)\n<doi:10.1007/s12076-012-0086-z>.",
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  "Maintainer": "Stamatis Kalogirou <stamatis.science@gmail.com>",
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        "NDMay12",
        "AllJune12",
        "AllMay12"
      ],
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      "table": true,
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    }
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        "lctools"
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      "title": "Spatial Interaction Models: Destination Accessibility",
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