Package: geoRglm
Version: 0.9-8
Date: 2015-04-23
Title: A Package for Generalised Linear Spatial Models
Authors@R: c(person(c("Ole", "F."), "Christensen", role = c("aut", "cre"),
email = "OleF.Christensen@mbg.au.dk"),
person(c("Paulo", "J."), "Ribeiro Jr", role = "aut",
email = "paulojus@ufpr.br"))
Depends: R (>= 2.2.0), geoR (>= 1.7-5), stats
Imports: sp
Suggests: coda
Description: Functions for inference in generalised linear spatial models. The posterior and predictive inference is based on Markov chain Monte Carlo methods. Package geoRglm is an extension to the package geoR, which must be installed first.
License: GPL (>= 2)
URL: http://gbi.agrsci.dk/~ofch/geoRglm
NeedsCompilation: yes
Packaged: 2015-04-24 13:50:42 UTC; paulojus
Author: Ole F. Christensen [aut, cre],
Paulo J. Ribeiro Jr [aut]
Maintainer: Ole F. Christensen <OleF.Christensen@mbg.au.dk>
Repository: CRAN
Date/Publication: 2015-04-27 09:05:25
Package: geofd
Version: 1.0
Author: Ramon Giraldo <rgiraldoh@unal.edu.co>, Pedro Delicado
<pedro.delicado@upc.edu>, Jorge Mateu <mateu@mat.uji.es>
Maintainer: Pedro Delicado <pedro.delicado@upc.edu>
Title: Spatial Prediction for Function Value Data
Description: Kriging based methods are used for predicting functional data
(curves) with spatial dependence.
License: GPL-2
Depends: fda, geoR
Repository: CRAN
Date: 2015-09-21
NeedsCompilation: no
Packaged: 2015-10-05 09:09:31 UTC; delicado
Date/Publication: 2015-10-05 12:58:15
Package: lmenssp
Type: Package
Title: Linear Mixed Effects Models with Non-Stationary Stochastic
Processes
Version: 1.1
Date: 2015-07-17
Author: Ozgur Asar, Peter J. Diggle
Maintainer: Ozgur Asar <o.asar@lancaster.ac.uk>
Depends: MASS, nlme, mvtnorm, geoR,
Description: Contains functions to estimate model parameters and filter, smooth and forecast random effects coefficients for mixed models with stationary and non-stationary stochastic processes under multivariate normal and t response distributions, diagnostic checks, bootstrap standard error calculation, etc.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2015-07-17 16:45:59 UTC; asar
Repository: CRAN
Date/Publication: 2015-07-17 19:58:17
Package: FeedbackTS
Type: Package
Title: Analysis of Feedback in Time Series
Version: 1.4
Date: 2016-05-06
Author: Samuel Soubeyrand, Cindy E. Morris, E. Keith Bigg
Maintainer: Samuel Soubeyrand <Samuel.Soubeyrand@avignon.inra.fr>
Description: Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
License: GPL (>= 2.0)
Depends: R (>= 3.0.0), maps, mapdata, proj4, sp, geoR
Imports: methods
NeedsCompilation: no
Packaged: 2016-05-10 11:11:30 UTC; soubeyra
Repository: CRAN
Date/Publication: 2016-05-10 19:04:59
Package: PrevMap
Type: Package
Title: Geostatistical Modelling of Spatially Referenced Prevalence Data
Version: 1.3.2
Date: 2015-12-01
Author: Emanuele Giorgi, Peter J. Diggle
Maintainer: Emanuele Giorgi <e.giorgi@lancaster.ac.uk>
Imports: splancs, truncnorm, methods
Depends: geoR, maxLik, raster, pdist
Description: It provides functions for both likelihood-based
and Bayesian analysis of spatially referenced prevalence data, and is
also an extension of the 'geoR' package.
LazyData: true
License: GPL (>= 2)
RoxygenNote: 5.0.0
NeedsCompilation: no
Packaged: 2015-12-04 11:01:11 UTC; giorgi
Repository: CRAN
Date/Publication: 2015-12-05 15:35:31
Package: aqfig
Type: Package
Title: Functions to help display air quality model output and
monitoring data
Version: 0.8
Date: 2013-11-08
Author: Jenise Swall, with contributions from Kristen Foley
Maintainer: Jenise Swall <jswall@vcu.edu>
Depends: geoR
Suggests: maps
Description: This package contains functions to help display air quality model output and monitoring data, such as creating color scatterplots, color legends, etc.
License: Unlimited
LazyLoad: yes
Packaged: 2013-11-08 22:38:00 UTC; jenise
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-11-09 11:34:23
providing 
.