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ecespa (version 1.1-17)

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

496

Version

1.1-17

License

GPL (>= 2)

Maintainer

Marcelino la Cruz

Last Published

January 5th, 2023

Functions in ecespa (1.1-17)

Test against non-Poisson (in-)homogeneous models

Mark-weighted K-function

Tests against 'independent labelling'

Simulation envelopes from the fitted values of a logistic model

Functions for spatial point pattern analysis in ecology

Loosmore and Ford Goodness of Fit Test

Differences between univariate and bivariate K-functions

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

Spatial point pattern of Helianthemum squamatum adult plants and seedlings

Dixon (2002) Nearest-neighbor contingency table analysis

ecespa-internal

Internal ecespa functions.

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

Mark-sum measure

Neighbourhood density function

Alive and dead oak trees

Fit the Poisson Cluster Point Process by Minimum Contrast

Syrjala test data

Artificial point data.

Simulate Poisson Cluster Process

Spatial point pattern of a plant community

Simulate Inhomogeneous Poisson Cluster Process

Cohorts of Helianthemum squamatum seedlings

Easily convert xy data to ppp format

Tree Species in a Swamp Forest

P-value for a discrete distribution on small sample data

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