README.md

In arh926/spWombling: Bayesian Wombling for Spatial Processes

spWombling: An R-package to perform Bayesian curvilinear curvature Wombling on spatial data

Reference to the paper titled, "Bayesian Modeling with Spatial Curvature Processes". Journal of the American Statistical Association (2023) (https://doi.org/10.1080/01621459.2023.2177166).

Illustration of curvilinear Bayesian Wombling on spatial data. Code for performing curvilinear Bayesian wombling on data ## Contents 1. Load the data and separate (a) co-ordinates (b) response (c) covariates 2. Fit a spatial Bayesian hierarchical model to the data 3. Perform gradient, curvature and posterior surface analysis 4. Locate or Annotate curves of interest 5. Perform rectilinear wombling at line segments Demonstration with synthetic data. ### Load the data and separate (a) co-ordinates (b) response (c) covariates wzxhzdk:0  ### Fit a spatial Bayesian hierarchical model to the data wzxhzdk:1 ### Perform gradient, curvature and posterior surface analysis wzxhzdk:2 #### True and Estimated Gradients  #### True and Estimated Curvature  ### Locate or Annotate curves of interest wzxhzdk:3 ### Perform rectilinear wombling at line segments wzxhzdk:4 #### Curvilinear Gradient  #### Curvilinear Curvature  ## Authors | Name | Email | | |:------ |:----------- | :----------- | | Aritra Halder (maintainer)| [e-mail] | Assistant Professor, Department of Biostatistics, Drexel University| | Sudipto Banerjee | [e-mail] | Professor and Chair, Department of Biostatistics, UCLA | | Dipak K. Dey | [e-mail] | Board of Trustees Distinguished Professor, Department of Statistics, UCONN |

arh926/spWombling documentation built on April 14, 2025, 4 p.m.