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ecespa (version 1.1-18)

Functions for Spatial Point Pattern Analysis

Description

Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on 'spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) and Olano et al. (2009) .

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Version

Install

install.packages('ecespa')

Monthly Downloads

621

Version

1.1-18

License

GPL (>= 2)

Maintainer

Marcelino la Cruz

Last Published

May 1st, 2025

Functions in ecespa (1.1-18)

Tests against 'independent labelling'

Lotwick's and Silverman's combined estimator of the marked K-function

Spatial point pattern of Helianthemum squamatum adult plants and seedlings

Differences between univariate and bivariate K-functions

Mark-weighted K-function

Test against non-Poisson (in-)homogeneous models

Simulation envelopes from the fitted values of a logistic model

Dixon (2002) Nearest-neighbor contingency table analysis

Functions for spatial point pattern analysis in ecology

Loosmore and Ford Goodness of Fit Test

Neighbourhood density function

Fit the Poisson Cluster Point Process by Minimum Contrast

Spatial point pattern of a plant community

ecespa-internal

Internal ecespa functions.

Easily convert xy data to ppp format

Artificial point data.

Mark-sum measure

P-value for a discrete distribution on small sample data

Simulate Poisson Cluster Process

Alive and dead oak trees

Fit the (In)homogeneous Poisson Cluster Point Process by Minimum Contrast

Simulate Inhomogeneous Poisson Cluster Process

Tree Species in a Swamp Forest

Cohorts of Helianthemum squamatum seedlings

Syrjala test data

Syrjala's test for the difference between the spatial distributions of two populations