Calculate the polygenic and major gene models for
quantitative trait linkage analysis using variance components
approach.  The 0.2.2 release includes bug fixes that allow
multic to run properly on 64-bit systems.  The 0.3.0 release
includes a fully implemented sw2mloci function.  Note to Splus
users: sw2mloci requires Splus 8 or greater.

Calculate the polygenic and major gene models for
quantitative trait linkage analysis using variance components
approach.  The 0.2.2 release includes bug fixes that allow
multic to run properly on 64-bit systems.  The 0.3.0 release
includes a fully implemented sw2mloci function.  As of 0.3.8,
Splus version is no longer supported.

Quantitative Linkage Analysis Tools using the Variance
Components Approach
Calculate the polygenic and major gene models for quantitative trait
linkage analysis using variance components approach. The 0.2.2
release includes bug fixes that allow 'multic' to run properly
on 64-bit systems. The 0.3.0 release includes a fully implemented
sw2mloci() function. As of 0.3.8, Splus version is no longer
supported. Additional documentation and examples can be found
in the inst directory.

A Companion to the Multi-CAST Collection
person("Nils Norman", "Schiborr", email = "nils-norman.schiborr@uni-bamberg.de", role=c("aut", "cre"))
Provides a basic interface for accessing annotation data from
the Multi-CAST collection, a database of spoken natural language texts
edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
diverse set of languages and has been annotated across multiple levels.
Annotation data is downloaded on request from the servers of the
Language Archive Cologne. See the Multi-CAST website
<https://lac.uni-koeln.de/multicast/> for more information and a list of
related publications.

person("Nils Norman", "Schiborr", email = "nils-norman.schiborr@uni-bamberg.de", role = c("aut", "cre"))

Provides a basic interface for accessing annotation data from
the Multi-CAST collection, a database of spoken natural language texts
edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
diverse set of languages and has been annotated across multiple levels.
Annotation data is downloaded on request from the servers of the
University of Bamberg. See the Multi-CAST website
<https://multicast.aspra.uni-bamberg.de/> for more information and a list
of related publications.