tag:crantastic.org,2005:/packages/FRKLatest activity for FRK2017-10-13T21:41:03Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/673312017-10-13T21:41:03Z2017-10-13T21:41:03ZFRK was upgraded to version 0.1.6<a href="/packages/FRK">FRK</a> was <span class="action">upgraded</span> to version <a href="/packages/FRK/versions/64392">0.1.6</a><br /><h3>Package description:</h3><p>Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach, discussed in Cressie and Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, and hence the covariance function, using a fixed set of n basis functions, where n is typically much smaller than the number of data points (or polygons) m. The method naturally allows for non-stationary, anisotropic covariance functions and the use of observations with varying support (with known error variance). The projected field is a key building block of the Spatial Random Effects (SRE) model, on which this package is based. The package FRK provides helper functions to model, fit, and predict using an SRE with relative ease.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/652822017-08-08T14:41:01Z2017-08-08T14:41:01ZFRK was upgraded to version 0.1.5<a href="/packages/FRK">FRK</a> was <span class="action">upgraded</span> to version <a href="/packages/FRK/versions/62444">0.1.5</a><br /><h3>Package description:</h3><p>Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach, discussed in Cressie and Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, and hence the covariance function, using a fixed set of n basis functions, where n is typically much smaller than the number of data points (or polygons) m. The method naturally allows for non-stationary, anisotropic covariance functions and the use of observations with varying support (with known error variance). The projected field is a key building block of the Spatial Random Effects (SRE) model, on which this package is based. The package FRK provides helper functions to model, fit, and predict using an SRE with relative ease.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/634932017-06-14T06:00:53Z2017-06-14T06:00:53ZFRK was upgraded to version 0.1.4<a href="/packages/FRK">FRK</a> was <span class="action">upgraded</span> to version <a href="/packages/FRK/versions/60725">0.1.4</a><br /><h3>Package description:</h3><p>Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach, discussed in Cressie and Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, and hence the covariance function, using a fixed set of n basis functions, where n is typically much smaller than the number of data points (or polygons) m. The method naturally allows for non-stationary, anisotropic covariance functions and the use of observations with varying support (with known error variance). The projected field is a key building block of the Spatial Random Effects (SRE) model, on which this package is based. The package FRK provides helper functions to model, fit, and predict using an SRE with relative ease.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/628802017-05-24T23:15:24Z2017-05-24T23:15:24Zcrantastic_production tagged FRK with Spatial<a href="/users/146">crantastic_production</a> <span class="action">tagged</span> <a href="/packages/FRK">FRK</a> with <a href="/task_views/Spatial">Spatial</a>crantastic_productiontag:crantastic.org,2005:TimelineEvent/622302017-05-05T03:20:56Z2017-05-05T03:20:56ZFRK was upgraded to version 0.1.3<a href="/packages/FRK">FRK</a> was <span class="action">upgraded</span> to version <a href="/packages/FRK/versions/59513">0.1.3</a><br /><h3>Package description:</h3><p>Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach, discussed in Cressie and Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, and hence the covariance function, using a fixed set of n basis functions, where n is typically much smaller than the number of data points (or polygons) m. The method naturally allows for non-stationary, anisotropic covariance functions and the use of observations with varying support (with known error variance). The projected field is a key building block of the Spatial Random Effects (SRE) model, on which this package is based. The package FRK provides helper functions to model, fit, and predict using an SRE with relative ease.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/617112017-04-21T08:20:45Z2017-04-21T08:20:45ZFRK was upgraded to version 0.1.2<a href="/packages/FRK">FRK</a> was <span class="action">upgraded</span> to version <a href="/packages/FRK/versions/59025">0.1.2</a><br /><h3>Package description:</h3><p>Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach, discussed in Cressie and Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, and hence the covariance function, using a fixed set of n basis functions, where n is typically much smaller than the number of data points (or polygons) m. The method naturally allows for non-stationary, anisotropic covariance functions and the use of observations with varying support (with known error variance). The projected field is a key building block of the Spatial Random Effects (SRE) model, on which this package is based. The package FRK provides helper functions to model, fit, and predict using an SRE with relative ease.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/592182017-02-05T13:00:42Z2017-02-05T13:00:42ZFRK was released<a href="/packages/FRK">FRK</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach, discussed in Cressie and Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, and hence the covariance function, using a fixed set of n basis functions, where n is typically much smaller than the number of data points (or polygons) m. The method naturally allows for non-stationary, anisotropic covariance functions and the use of observations with varying support (with known error variance). The projected field is a key building block of the Spatial Random Effects (SRE) model, on which this package is based. The package FRK provides helper functions to model, fit, and predict using an SRE with relative ease.</p>crantastic.org