How To Install "r-cran-fields" Package on Ubuntu
Quick Install Instructions of r-cran-fields on Ubuntu Server. It’s Super Easy! simply click on Copy button to copy the command and paste into your command line terminal using built-in APT package manager.
See below for quick step by step instructions of SSH commands, Copy/Paste to avoid miss-spelling or accidently installing a different package.
See below for quick step by step instructions of SSH commands, Copy/Paste to avoid miss-spelling or accidently installing a different package.
Quick Install Steps:
Step 1
sudo apt-get update -y
Step 2
sudo apt-get install -y r-cran-fields
Step 3
Check the system logs to confirm that there are no related errors.
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see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.
Execute the commands above step by step.
You can simply hit the copy button to copy the command and paste into the command line interface.
Note: -y flag means to assume yes and silently install, without asking you questions in most cases.
Note: -y flag means to assume yes and silently install, without asking you questions in most cases.
GNU R tools for spatial data
For curve, surface and function fitting with an emphasis on splines, spatial data and spatial statistics. The major methods include cubic, and thin plate splines, Kriging and compact covariances for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range). A major feature is that any covariance function implemented in R and following a simple fields format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets. GNU R tools for spatial data
For curve, surface and function fitting with an emphasis on splines, spatial data and spatial statistics. The major methods include cubic, and thin plate splines, Kriging and compact covariances for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range). A major feature is that any covariance function implemented in R and following a simple fields format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets.
For curve, surface and function fitting with an emphasis on splines, spatial data and spatial statistics. The major methods include cubic, and thin plate splines, Kriging and compact covariances for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range). A major feature is that any covariance function implemented in R and following a simple fields format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets. GNU R tools for spatial data
For curve, surface and function fitting with an emphasis on splines, spatial data and spatial statistics. The major methods include cubic, and thin plate splines, Kriging and compact covariances for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range). A major feature is that any covariance function implemented in R and following a simple fields format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets.
Detailed Instructions:
Step 1
Run update command to update package repositories and get latest package information.
sudo apt-get update -y
Step 2
Run the install command with -y flag to quickly install the packages and dependencies.
sudo apt-get install -y r-cran-fields
Step 3
Check the system logs to confirm that there are no related errors.
You can use
ZoomAdmin
to check the logs, manager servers, host multiple websites and apps on your servers and more. The apps run in docker containers, to learn more
see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.
see ZoomAdmin Features for list of features and demo videos. And you can start with the Free Plan.