Wraps the Boost odeint library for integration of differential equations.

Wraps the Boost 'odeint' library for integration of differential equations.

Wraps the Boost odeint library for integration of differential
equations.

Solvers for Ordinary Differential Equations
This package provides an interface for the ODE solver lsoda.
ODEs are expressed as R functions or as compiled code.

This package provides an interface for the ODE solver
lsoda.  ODEs are expressed as R functions or as compiled code.
This is deprecated!  Use deSolve instead.

R. Woodrow Setzer <setzer.woodrow@epa.gov> with fixups by Martin Maechler <maechler@stat.math.ethz.ch>
This package provides an interface for the ODE solver lsoda.
ODEs are expressed as R functions.

This package provides an interface for the ODE solver
lsoda.  ODEs are expressed as R functions or as compiled code.
This is deprecated and will disappear from CRAN by the end of
2012!  Use deSolve instead.

Outlier Detection Using Partitioning Clustering Algorithms
c(person(given="Zeynel", family="Cebeci", email = "zcebeci@cukurova.edu.tr", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7641-7094")),
person(given="Cagatay", family="Cebeci", email = "cagatay.cebeci@strath.ac.uk", role = "ctb", comment = c(ORCID = "0000-0003-2644-1261")),
person("Yalcin", "Tahtali", email = "yalcin.tahtali@gop.edu.tr", role = "ctb", comment = c(ORCID = "0000-0003-0012-0611")))
An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.