3 packages on CRAN
Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details.
Implementation of a computationally efficient method for simulating queues with arbitrary arrival and service times.
Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex datasets with intercorrelated dependent and independent variables. Here we implement a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale.