c(person("Jay", "Ver Hoef", role = c("aut", "cre"), email = "ver.hoef@SpatialStreamNetworks.com"),
person("Erin", "Peterson", role = c("aut"), email = "peterson@SpatialStreamNetworks.com"))
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., 2010. <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood.  Mapping and other graphical functions are included.
Jay Ver Hoef [aut, cre],
Erin Peterson [aut]

MASS, igraph (>= 0.6), maptools, lattice, sp, methods

MASS, igraph (>= 0.6), maptools, lattice, methods

MASS, igraph (>= 1.0.0), maptools, lattice, methods

MASS, igraph (>= 1.0.0), maptools, lattice, methods, rgeos,
Matrix

MASS, igraph (>= 1.0.0), maptools, lattice, methods,
filematrix, foreach, Matrix, rgdal, rgeos

R (>= 2.10), methods, maptools, RSQLite, igraph (>= 0.6),
MASS, sp, BH
Geostatistical modeling for data on stream networks,
including models based on in-stream distance.  Models are
created using moving average constructions. Spatial linear
models, including covariates, can be fit with ML or REML.
Mapping and other graphical functions are included.
Removed as it has unstated and rather restrictive
system requirements.

Bayesian Spatio-Temporal Analysis in Stream Networks
c(person("Edgar", "Santos-Fernandez", role = c("aut", "cre","cph"),
email = "santosfe@qut.edu.au") )
Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2021)."Bayesian spatio-temporal models for stream networks" <arXiv:2103.03538>. In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.

A package that provides a client interface to SOAP (Simple
Object Access Protocol) servers from within R.
ClassDefns.R fault.S http.S parseSOAP.S print.S RPC.R SOAP.S
SOAPServer.S zSOAP.S duration.S nativeWrite.R
functionAccessors.R writeGeneratedCode.R wsdlProcess.R utils.R
toSOAP.R httpGet.R wsse.R
There is an opportunity to do a lot of optimization in the
machine generated code to reduce the processing time. This is a
simple approach that "works" with little regard for
optimization.