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        *  Changes and Developments in the nFactors Package  *
        *                                                    *

- Changes in nFactors 2.4.0 (2020-03-20) -
Use of the function methodss::class1() instead of the function class().

Transition to Roxygen documentation.

- Changes in nFactors 2.3.3 (2011-12-16) -
 Changes done because the functions mean() and sd() in the moreStats() function
 were depreciated with data.frames.

 xMean <- sapply(x, mean) # mean(x) before change
 xSd   <- sapply(x, sd)   # sd(x) before change

- Changes in nFactors 2.3.2 (2010-10-04) -

 Changes in this version are stricly limited to orthographical correction
 of the documentation.

- Changes in nFactors 2.3.1 (2009-14-10) -

  o  Two bugs were fixed inside the function structureSim. The first one, at
     line 21, is related to permutation analysis where the quantile paramater
     of the function call stayed fixed at 0.95 even if the value is different
     in the call. The variable quantile is now formally use in the call to
     eigenBootParallel. The second bug was realted to an error in the column
     names of the data.frame return from structureSim: the per and mean.eig
     columns were unfortunalely inrerchanged. Version 2.3.1 fixed it.

  o  Bug fixing was the occasion to introduce a new function used for a
     paper proposed to Behaviormetrika, studySim. But this function is
     useful for many other simulation settings.

- Changes in nFactors 2.3 (2009-15-09) -

  This version of nFactors is a major upgrade and so presents important
  additions and modifications. Care was taken to not modified parameters from
  version 2.2 calls so that functions and packages already requiring nFactors
  will yet operate correctly in the future. But like with all mojor upgrades,
  care must be taken and it is recommanded that developpers verify their
  results. All the future upgrades won't have this potentiel problems.

  o  Many new procedures to determine the number of components or factors to
     retain are added: permutation and bootstrap parallel analysis, CNG,
     Bentler and Yuan, Bartlett, Anderson, Lawley, Zosky and Jurs, etc.

  o  Care is taken to uniformise the labelling of new functions and new
     variables. According to the Java coding practice, with this labelling, the
     names begin with a small character, and capitals are used inside for added

  o  It is now possible to do most of the nfactors package analysis on a
     covariance matrix.

  o  It is now possible to do most of the nfactors package analysis in the CFA

  o  The permutation parallel analysis of Buja and Eyuboglu (1992) is added.

  o  It is now possible to bootstrap the eigenvalues from an empirical data

  o  New heuristic numerical indices are added to determine the number of
     components/factores to retain: CNG, Zoski and Jurs multiple regression,
     Joski and Jurs standard error of the scree, and Nelson R.

  o  Likelihood ratio tests are added: Bartlet, Anderson, Lawley, and Bentler
     and Yuan chi-squared.

  o  The eigenComputes function computes eigenvalues conditional of the class
     of the object from which data come from: eigenvalues from vector,
     correlation/covariance matrix, or data from a data.frame.

  o  The eigenFrom function determine the class of the object.

  o  The corFA function is added to insert commulalities in the diagonal of a
     correlation or a covariance matrix.

  o  The makeCor function creates a full correlation/covariance matrix from a
     matrix with lower part filled and upper part with zeros.

  o  Functions are added to generate a factor structure (generateStructure)
     and to simulate data and correlation matrices from a predefined factor
     structure (structureSim).

  o  A function, moreStats, is added to be computes additionnal statistics on a
     numeric data.frame.

  o  Utility functions for \code{nScree} class objects werw implemented:
     is.nScree, plot.nScree, plot.nScree and summary.nScree.

- Changes in nFactors 2.2 (2009-02-06) -

  o  Considering the instabillity of the function factanal with ill
     conditionned correlation matrices, new functions for computing factor
     analysis are added: componentAxis, iteratePrincipalAxix, principalAxis and

  o  The diagReplace function replace the upper or the lower diagonal of a
     correlation matrix with the respective lower or lower diagonal.

  o  The rRecovery function is added for a verification of the quality of the
     recovery of an initial correlation matrix.

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