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ecespa (version 1.1-12)

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

1.1-12

License

GPL (>= 2)

Maintainer

Marcelino la Cruz

Last Published

October 25th, 2020

Functions in ecespa (1.1-12)

Tests against 'independent labelling'

Loosmore and Ford Goodness of Fit Test

Dixon (2002) Nearest-neighbor contingency table analysis

Fit the Poisson Cluster Point Process by Minimum Contrast

Mark-weighted K-function

Alive and dead oak trees

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

Easily convert xy data to ppp format

Spatial point pattern of Helianthemum squamatum adult plants and seedlings

Spatial point pattern of a plant community

Functions for spatial point pattern analysis in ecology

Syrjala test data

Artificial point data.

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

Neighbourhood density function

Simulate Poisson Cluster Process

Test against non-Poisson (in-)homogeneous models

Mark-sum measure

Simulation envelopes from the fitted values of a logistic model

Tree Species in a Swamp Forest

P-value for a discrete distribution on small sample data

ecespa-internal

Internal ecespa functions.

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

Differences between univariate and bivariate K-functions

Cohorts of Helianthemum squamatum seedlings

Simulate Inhomogeneous Poisson Cluster Process