Ripley's K function is originally applied to the second order analysis of stationary point process. It can be used to summarize a point pattern, test hypothesis about the pattern, estimate parameters and fit models. The relevance research has penetrated into bivaritate and multivariate generalizations, which provides effective methods to describe relationships between two or more point patterns.
An implementation of Ripley's K is provided here, supporting multiple pattern of massive points with various CSR and hypothesis testing methods.
- Univariate Pattern
- Bivariate Pattern
- Spatiotemporal Pattern
- Homogeneous Possion Process
- Heterogenous Possion Process
- Cox Process
- Markov Process
- Chi-Squared Test
- Kolmogorov–Smirnov (K-S) Test
- Monte Carlo Simulation
Coming Soon...