ecespa-package |
Functions for spatial point pattern analysis in ecology |
dixon2002 |
Dixon (2002) Nearest-neighbor contingency table analysis |
ecespa |
Functions for spatial point pattern analysis in ecology |
ecespa.kci |
Test against non-Poisson (in-)homogeneous models |
ecespa.kmm |
Mark-weighted K-function |
ecespa.minconfit |
Fit the (In)homogeneous Poisson Cluster Point Process by Minimum Contrast |
fig1 |
Artificial point data. |
fig2 |
Artificial point data. |
fig3 |
Artificial point data. |
figuras |
Artificial point data. |
getis |
Neighbourhood density function |
gypsophylous |
Spatial point pattern of a plant community |
haz.ppp |
Easily convert xy data to ppp format |
Helianthemum |
Spatial point pattern of Helianthemum squamatum adult plants and seedlings |
ipc.estK |
Fit the (In)homogeneous Poisson Cluster Point Process by Minimum Contrast |
K012 |
Tests against 'independent labelling' |
K1K2 |
Differences between univariate and bivariate K-functions |
Kci |
Test against non-Poisson (in-)homogeneous models |
Kclust |
Fit the Poisson Cluster Point Process by Minimum Contrast |
Ki |
Test against non-Poisson (in-)homogeneous models |
Kinhom.log |
Simulation envelopes from the fitted values of a logistic model |
Kmm |
Mark-weighted K-function |
Kmulti.ls |
Lotwick's and Silverman's combined estimator of the marked K-function |
LF.gof |
Loosmore and Ford Goodness of Fit Test |
marksum |
Mark-sum measure |
p2colasr |
P-value for a discrete distribution on small sample data |
pc.estK |
Fit the Poisson Cluster Point Process by Minimum Contrast |
plot.ecespa.getis |
Neighbourhood density function |
plot.ecespa.kci |
Test against non-Poisson (in-)homogeneous models |
plot.ecespa.kmm |
Mark-weighted K-function |
plot.ecespa.marksum |
Mark-sum measure |
plot.ecespa.minconfit |
Fit the (In)homogeneous Poisson Cluster Point Process by Minimum Contrast |
plot.ecespa.syrjala |
Syrjala's test for the difference between the spatial distributions of two populations |
plot.syrjala.test |
Syrjala's test for the difference between the spatial distributions of two populations |
print.ecespa.getis |
Neighbourhood density function |
print.ecespa.kci |
Test against non-Poisson (in-)homogeneous models |
print.ecespa.kmm |
Mark-weighted K-function |
print.ecespa.marksum |
Mark-sum measure |
print.ecespa.minconfit |
Fit the (In)homogeneous Poisson Cluster Point Process by Minimum Contrast |
print.ecespa.syrjala |
Syrjala's test for the difference between the spatial distributions of two populations |
print.syrjala.test |
Syrjala's test for the difference between the spatial distributions of two populations |
quercusvm |
Alive and dead oak trees |
rIPCP |
Simulate Inhomogeneous Poisson Cluster Process |
seedlings |
Cohorts of Helianthemum squamatum seedlings |
seedlings1 |
Cohorts of Helianthemum squamatum seedlings |
seedlings2 |
Cohorts of Helianthemum squamatum seedlings |
sim.poissonc |
Simulate Poisson Cluster Process |
swamp |
Tree Species in a Swamp Forest |
syr1 |
Syrjala test data |
syr2 |
Syrjala test data |
syr3 |
Syrjala test data |
syrjala |
Syrjala's test for the difference between the spatial distributions of two populations |
syrjala.test |
Syrjala's test for the difference between the spatial distributions of two populations |
syrjala0 |
Syrjala's test for the difference between the spatial distributions of two populations |