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ecespa (version 1.1-16)

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

407

Version

1.1-16

License

GPL (>= 2)

Maintainer

Marcelino la Cruz

Last Published

November 9th, 2022

Functions in ecespa (1.1-16)

Mark-weighted K-function

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

Test against non-Poisson (in-)homogeneous models

Simulation envelopes from the fitted values of a logistic model

Functions for spatial point pattern analysis in ecology

Dixon (2002) Nearest-neighbor contingency table analysis

Loosmore and Ford Goodness of Fit Test

Artificial point data.

Tests against 'independent labelling'

Spatial point pattern of Helianthemum squamatum adult plants and seedlings

ecespa-internal

Internal ecespa functions.

Neighbourhood density function

P-value for a discrete distribution on small sample data

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

Easily convert xy data to ppp format

Tree Species in a Swamp Forest

Fit the Poisson Cluster Point Process by Minimum Contrast

Alive and dead oak trees

Mark-sum measure

Spatial point pattern of a plant community

Simulate Inhomogeneous Poisson Cluster Process

Simulate Poisson Cluster Process

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

Cohorts of Helianthemum squamatum seedlings

Syrjala test data

Differences between univariate and bivariate K-functions