This R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: *Costanzo S., et al. - Creating the funerary landscape in eastern Sudan.
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Updated
Jul 8, 2021
This R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: *Costanzo S., et al. - Creating the funerary landscape in eastern Sudan.
MarrowMap is an R package derived from the spatstat package which has been designed to create a workflow for spatial point pattern analysis of bone marrow samples.
Dynamic Recursive Point Pattern Matching Algorithm for CBIR
R script code developed by F. Brandolini & F. Carrer to accompany the paper: Filippo Brandolini & Francesco Carrer (2020) "Terra, Silva et Paludes. Assessing the Role of Alluvial Geomorphology for Late-Holocene Settlement Strategies (Po Plain – N Italy) Through Point Pattern Analysis
Companion for the 2024 manuscript in Spatial and Spatio-temporal Epidemiology entitled "Multiple 'spaces': using wildlife surveillance, climatic variables, and spatial statistics to identify and map a climatic niche for endemic plague in California, U.S.A."
Inhomogeneous higher-order summary statistics for point processes on linear networks
Point pattern analysis library and command line tool in Rust
Environmental Interpolation using Spatial Kernel Density Estimation
Replication data for point pattern analysis of airplane accidents in Florida (2014)
The R Shiny App for machine learning analysis and visualization of cellular spatial point patterns under hypercaloric diet shifts.
Point patterns for the GeoStats.jl framework
R Package providing the Adapted Pair Correlation Function
sub-package of spatstat containing core functionality for data analysis and modelling
Spatio-temporal point patterns on linear networks
Spatial analysis and simulation of ecological communities
📦 Analyse species-habitat associations in R
A novel Clustering algorithm by measuring Direction Centrality (CDC) locally. It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points.
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