Package: ecespa 1.1-17

Marcelino de la Cruz Rot

ecespa: Functions for Spatial Point Pattern Analysis

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) <doi:10.1111/j.0906-7590.2008.05299.x> and Olano et al. (2009) <doi:10.1051/forest:2008074>.

Authors:Marcelino de la Cruz Rot, with contributions of Philip M. Dixon and Jose M. Blanco-Moreno

ecespa_1.1-17.tar.gz
ecespa_1.1-17.tar.gz(r-4.5-noble)ecespa_1.1-17.tar.gz(r-4.4-noble)
ecespa_1.1-17.tgz(r-4.4-emscripten)ecespa_1.1-17.tgz(r-4.3-emscripten)
ecespa.pdf |ecespa.html✨
ecespa/json (API)

# Install 'ecespa' in R:
install.packages('ecespa', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • Helianthemum - Spatial point pattern of Helianthemum squamatum adult plants and seedlings
  • fig1 - Artificial point data.
  • fig2 - Artificial point data.
  • fig3 - Artificial point data.
  • gypsophylous - Spatial point pattern of a plant community
  • quercusvm - Alive and dead oak trees
  • seedlings1 - Cohorts of Helianthemum squamatum seedlings
  • seedlings2 - Cohorts of Helianthemum squamatum seedlings
  • swamp - Tree Species in a Swamp Forest
  • syr1 - Syrjala test data
  • syr2 - Syrjala test data
  • syr3 - Syrjala test data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

20 exports 0.61 score 20 dependencies 1 dependents 1 mentions 31 scripts 505 downloads

Last updated 2 years agofrom:3ab1561395. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 23 2024
R-4.5-linux-x86_64OKAug 23 2024

Exports:dixon2002getishaz.pppipc.estKK012K1K2KciKclustKiKinhom.logKmmKmulti.lsLF.gofmarksumpc.estKrIPCPsim.poissoncsyrjalasyrjala.testsyrjala0

Dependencies:abinddeldirgoftestlatticeMatrixmgcvnlmepolycliprpartspatstatspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilstensor

Readme and manuals

Help Manual

Help pageTopics
Dixon (2002) Nearest-neighbor contingency table analysisdixon2002
Functions for spatial point pattern analysis in ecologyecespa-package ecespa
Artificial point data.fig1 fig2 fig3 figuras
Neighbourhood density functiongetis plot.ecespa.getis print.ecespa.getis
Spatial point pattern of a plant communitygypsophylous
Easily convert xy data to ppp formathaz.ppp
Spatial point pattern of Helianthemum squamatum adult plants and seedlingsHelianthemum
Fit the (In)homogeneous Poisson Cluster Point Process by Minimum Contrastecespa.minconfit ipc.estK plot.ecespa.minconfit print.ecespa.minconfit
Tests against 'independent labelling'K012
Differences between univariate and bivariate K-functionsK1K2
Test against non-Poisson (in-)homogeneous modelsecespa.kci Kci Ki plot.ecespa.kci print.ecespa.kci
Simulation envelopes from the fitted values of a logistic modelKinhom.log
Mark-weighted K-functionecespa.kmm Kmm plot.ecespa.kmm print.ecespa.kmm
Lotwick's and Silverman's combined estimator of the marked K-functionKmulti.ls
Loosmore and Ford Goodness of Fit TestLF.gof
Mark-sum measuremarksum plot.ecespa.marksum print.ecespa.marksum
P-value for a discrete distribution on small sample datap2colasr
Fit the Poisson Cluster Point Process by Minimum ContrastKclust pc.estK
Alive and dead oak treesquercusvm
Simulate Inhomogeneous Poisson Cluster ProcessrIPCP
Cohorts of Helianthemum squamatum seedlingsseedlings seedlings1 seedlings2
Simulate Poisson Cluster Processsim.poissonc
Tree Species in a Swamp Forestswamp
Syrjala's test for the difference between the spatial distributions of two populationsplot.ecespa.syrjala plot.syrjala.test print.ecespa.syrjala print.syrjala.test syrjala syrjala.test syrjala0
Syrjala test datasyr1 syr2 syr3