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CircSpaceTime

Spatial and Spatio-Temporal Bayesian Model for Circular Data

Implementation of Bayesian models for spatial and spatio-temporal interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions.

Currently the following models are implemented:
Spatial Wrapped Normal
Spatial Projected Normal
Spatio-Temporal Wrapped Normal
Spatio-Temporal Projected Normal

Installation

From source

If you are linux/linux-like users or simply you want to compile from source the best way is to use "devtools"

devtools_installed <- require(devtools)
 if (!devtools_installed){
   install.packages("devtools", dep = TRUE)
    library(devtools)
    }
  install_github("santoroma/CircSpaceTime")  

Dependencies: Rcpp, RcppArmadillo, circular, ggplot2, coda
Suggested: foreach, parallel, iterators, doParallel, knitr, rmarkdown, gridExtra

From CRAN

The package is in submission on CRAN.

  install.packages("CircSpaceTime", dep = TRUE)

Using the package

library(CircSpaceTime)

For further information on the package you can read the help or take a look at the vignette

Issues

Please help us to improve the package!
For any issue/error/"what is this?" report the best way is to visit the issues page and:

  1. Find if already exist a similar issue, read it and if the case write a precise comment with reproducible example.
  2. If not, open a new one writing a precise comment with reproducible example.

Thanks

Mario, Gianluca and Giovanna

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Version

Install

install.packages('CircSpaceTime')

Monthly Downloads

50

Version

0.9.0

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Mario Santoro

Last Published

June 6th, 2019

Functions in CircSpaceTime (0.9.0)

WrapSp

Samples from the Wrapped Normal spatial model
WrapSpTi

Samples from the posterior distribution of the Wrapped Normal spatial temporal model
rose_diag

Rose diagram in ggplot2 inspired from rose.diag in package circular.
may

May waves.
april

April waves.
ProjKrigSpTi

#' Spatio temporal interpolation using projected spatial temporal normal model.
ProjKrigSp

Kriging using projected normal model.
WrapKrigSp

Spatial interpolation using wrapped normal model.
APEcirc

Average Prediction Error for circular Variables.
CRPScirc

The Continuous Ranked Probability Score for Circular Variables.
WrapKrigSpTi

Prediction using wrapped normal spatio-temporal model.
CircSpaceTime

CircSpaceTime: implementation of Bayesian models, for spatial and spatio-temporal interpolation of circular data.
ConvCheck

Testing Convergence of mcmc using package coda
ProjSp

Samples from the Projected Normal spatial model
ProjSpTi

Samples from the posterior distribution of the Projected Normal spatial model