Correctness of generic modules

2005 ◽  
pp. 406-417 ◽  
Author(s):  
Wolfgang Reif
Keyword(s):  
Generic Modules

Generic modules and tameness

2011 ◽  
pp. 335-344
Author(s):  
R. Bautista ◽  
L. Salmeron ◽  
R. Zuazua
Keyword(s):  
Generic Modules

Generic modules

1988 ◽  
pp. 162-173
Author(s):  
Winfried Bruns ◽  
Udo Vetter
Keyword(s):  
Generic Modules

Generic modules for tilted algebras

1997 ◽  
Vol 42 (3) ◽  
pp. 177-180
Author(s):  
Xianneng Du
Keyword(s):  
Tilted Algebras ◽  
Generic Modules

Generic modules over the quantum groupU t (sl(2)) att a root of unit

1994 ◽  
Vol 83 (1) ◽  
pp. 75-98 ◽  
Author(s):  
Xiao Jie
Keyword(s):  
Generic Modules

ON ABSTRACTION AND SHARING IN GENERIC MODULES

1995 ◽  
pp. 22-41
Author(s):  
JUN SHEN ◽  
GORDON V. CORMACK ◽  
DOMINIC DUGGAN
Keyword(s):  
Generic Modules

Stable Equivalence and Generic Modules

2000 ◽  
Vol 32 (5) ◽  
pp. 615-618 ◽  
Author(s):  
Henning Krause ◽  
Grzegorz Zwara
Keyword(s):  
Stable Equivalence ◽  
Generic Modules

scholarly journals QDECR: A Flexible, Extensible Vertex-Wise Analysis Framework in R

2021 ◽  
Vol 15 ◽  
Author(s):  
Sander Lamballais ◽  
Ryan L. Muetzel
Keyword(s):  
Modular Design ◽  
Large Population ◽  
R Package ◽  
Epidemiological Studies ◽  
Population Based ◽  
Design Matrix ◽  
Linear Modeling ◽  
Generic Modules ◽  
Cortical Morphology

The cerebral cortex is fundamental to the functioning of the mind and body. In vivo cortical morphology can be studied through magnetic resonance imaging in several ways, including reconstructing surface-based models of the cortex. However, existing software for surface-based statistical analyses cannot accommodate “big data” or commonly used statistical methods such as the imputation of missing data, extensive bias correction, and non-linear modeling. To address these shortcomings, we developed the QDECR package, a flexible and extensible R package for group-level statistical analysis of cortical morphology. QDECR was written with large population-based epidemiological studies in mind and was designed to fully utilize the extensive modeling options in R. QDECR currently supports vertex-wise linear regression. Design matrix generation can be done through simple, familiar R formula specification, and includes user-friendly extensions for R options such as polynomials, splines, interactions and other terms. QDECR can handle unimputed and imputed datasets with thousands of participants. QDECR has a modular design, and new statistical models can be implemented which utilize several aspects from other generic modules which comprise QDECR. In summary, QDECR provides a framework for vertex-wise surface-based analyses that enables flexible statistical modeling and features commonly used in population-based and clinical studies, which have until now been largely absent from neuroimaging research.

Tame Algebras and Generic Modules

1991 ◽  
Vol s3-63 (2) ◽  
pp. 241-265 ◽  
Author(s):  
William Crawley-Boevey
Keyword(s):  
Generic Modules ◽  
Tame Algebras
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