file-based access to large matrices stored on HDD in binary
format
a package providing interface to C++ FILEVECTOR library
facilitating analysis of large (giga- to tera-bytes) matrices;
matrix storage is organized in a way that either columns or
rows are quickly accessible; primarily aimed to support
genome-wide association analyzes e.g. using GenABEL and
ProbABEL

a package providing interface to C++ FILEVECTOR library
facilitating analysis of large (giga- to tera-bytes) matrices;
matrix storage is organized in a way that either columns or
rows are quickly accessible; primarily aimed to support
genome-wide association analyzes e.g. using GenABEL, MixABEL
and ProbABEL

Yurii Aulchenko, Stepan Yakovenko, Erik Roos, Marcel Kempenaar
a package providing an interface to the C++ FILEVECTOR
library facilitating analysis of large (giga- to tera-bytes)
matrices; matrix storage is organized in a way that either
columns or rows are quickly accessible; primarily aimed to
support genome-wide association analyses e.g. using GenABEL,
MixABEL and ProbABEL

Yurii Aulchenko, Stepan Yakovenko, Erik Roos, Marcel Kempenaar, Maksim
Struchalin
a package providing an interface to the C++ FILEVECTOR
library facilitating analysis of large (giga- to tera-bytes) matrices;
matrix storage is organized in a way that either columns or rows are
quickly accessible; primarily aimed to support genome-wide association
analyses e.g. using GenABEL, MixABEL and ProbABEL

File-Based Access to Large Matrices Stored on HDD in Binary
Format
Yurii Aulchenko, Stepan Yakovenko, Erik Roos, Marcel Kempenaar, Maksim
Struchalin, Lennart C. Karssen
Provides an interface to the C++ FILEVECTOR library
facilitating analysis using large (giga- to tera-bytes) matrices.
Matrix storage is organized in a way that either columns or rows
are quickly accessible. DatABEL is primarily aimed to support
genome-wide association analyses e.g. using GenABEL, MixABEL and
ProbABEL.

For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) <doi:10.20944/preprints201710.0098.v1>.