##### https://github.com/cran/nFactors

Tip revision:

**592b098fc786911733da1c1953e58c9d1c2e9517**authored by**Gilles Raiche**on**10 April 2010, 00:00:00 UTC****version 2.3.3** Tip revision:

**592b098** NEWS

```
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* *
* Changes and Developments in the nFactors Package *
* *
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- Changes in nFactors 2.3.3 (2011-12-16) -
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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
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- Changes in nFactors 2.3.2 (2010-10-04) -
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Changes in this version are stricly limited to orthographical correction
of the documentation.
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- Changes in nFactors 2.3.1 (2009-14-10) -
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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.
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- Changes in nFactors 2.3 (2009-15-09) -
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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
concepts.
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
context.
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
matrix.
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.
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- Changes in nFactors 2.2 (2009-02-06) -
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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
principalComponents.
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.
```