\name{ProficiencyModel}
\alias{ProficiencyModel}
\title{An abstract class representing of the proficiency space for a group of examinees
}
\description{

  This is an abstract class that contains information about how
  proficiency is represented at the initial time point for a group of
  examinees.  It provides information about the number of latent
  variables, and mechanisms for drawing latent variables and evaluating
  the prior (population) probability of a given configuration.
  
}
\usage{
ProficiencyModel(...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{\dots}{
    Initial values for the object:  these are usually population
    parameters and vary with the implementation.
  }
}
\details{

  This is a proficiency model in the Evidence Centered Design
  framework.  It contains population information about the space of
  possible proficiencies. The assumption is that proficiency is
  represented with a vector valued latent variable.  The generic
  funciton \code{\link{nlatent}} returns the size of this vector.

  It is a subclass of \code{\link{AbstractModel}} which supplies the
  \code{param} slot and the \code{\link{parameters}} method for
  accessing it.

  Generally speaking there will be a population parameter associated
  with this model.  Often the goal of a calibration exercise is to
  estimate this parameter.  The generic function
  \code{\link{drawPMParam}} samples a value of this parameter.

  Given the parameter it is possible to calculate the prior probability
  of a given proficiency profile (value of the latent variable) or draw
  a random profile.  The functions \code{\link{drawInitialLatent}} and
  \code{\link{lpriorLatent}} perform those operations.
  
}
\value{
  This is an abstract class, and must be extended by other classes.  It
  is an extension of \code{\link{AbstractModel}} which supplies the
  \code{param} and \code{paramType} slots.
}
\references{
%% ~put references to the literature/web site here ~
}
\author{Russell Almond
}
\seealso{
  Methods:
  \code{\link{nlatent}}, \code{\link{drawPMParam}},
  \code{\link{drawInitialLatent}}, \code{\link{lpriorLatent}}


  Superclasses:
  \code{\link{AbstractModel}}

  Subclasses:
  \code{\link{TimelessNormalPM}}

  Extensions:
  \code{\link{ProficiencyGrowthModel}}

  
}
\examples{

## Must be extended to be used.  See TimelessNormalPM.R for example.

}
\keyword{ distribution }
\keyword{ class }