| Name : R-picR
| |
| Version : 1.0.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.7
| Date : 2024-06-14 11:11:16
|
| Group : Development/Libraries/Other
| Source RPM : R-picR-1.0.0-lp153.1.7.src.rpm
|
| Size : 0.04 MB
| |
| Packager : (none)
| |
| Summary : Predictive Information Criteria for Model Selection
|
Description :
Computation of predictive information criteria (PIC) from select model
object classes for model selection in predictive contexts. In contrast
to the more widely used Akaike Information Criterion (AIC), which are
derived under the assumption that target(s) of prediction (i.e.
validation data) are independently and identically distributed to the
fitting data, the PIC are derived under less restrictive assumptions
and thus generalize AIC to the more practically relevant case of
training/validation data heterogeneity. The methodology featured in
this package is based on Flores (2021)
< https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST>
\"A new class of information criteria for improved prediction in the
presence of training/validation data heterogeneity\".
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
