* version 1.1-0
* fix code to have the package back on CRAN
* fix code to comply with CRAN remarks during the submission process
* version 1.0-5
* in util.R:
1) computeScores():
add res$correlation = correlation
add res$reducedCorrelation = NULL
change res$eigenvalues = eigen(S)$values
* in factorScorePca.R:
1) add res$reducedCorrelation = NULL,
2) change res$eigenvalues = eigen(S)$values
* in factorScorePfa.R:
1) res$covariance = covariance
2) res$correlation = correlation
3) res$reducedCorrelation = reducedCorrelation
4) change res$eigenvalues = eigen(S)$values
* in AllClasses.R:
1) add slots for Fa: cor, reducedCorrelation
* in FaClassic.R and FaCov.R
1) add slot: cor, reducedCorrelation
* in Fa.R
1) revise method: summary
* version 1.0-4
* large revision
* in util.R:
1) add compute_cov_cor()
2) revise computeScores()
* in factorScorePca.R and factorScorePfa.R:
1) add a variable scaledX
2) change the formula of computation of F
3) add cor argument
4) add the following components of return value: covariance, usedMatrix
* in FaClassic.R and FaCov.R
1) add cor argument
2) add the following codes:
if(scoresMethod != "none" && method == "mle")
out <- computeScores(out, x = data, covmat = covmat, cor = cor, scoresMethod = scoresMethod)
3) add slots: covariance, usedMatrix
* in AllClasses.R
1) add slots for Fa: covariance, usedMatrix
* in Fa.R
1) revise getFa()
2) revise summary()
3) revise predict()
* version 1.0-03
* DESCRIPTION:
1) Depends: methods, robustbase, pcaPP, mvtnorm, rrcov, R (>= 2.15.0)
Because the most recent robustfa 1.0-02 update does not work with R-oldrelease (i.e. R-2.14.2).
* version 1.0-02
* DESCRIPTION:
1) Depends: methods, robustbase, pcaPP, mvtnorm, rrcov
2) Imports: stats4, stats
3) Suggests: grid, lattice, cluster, mclust, MASS, ellipse
* NAMESPACE:
1) importFrom(stats4, plot, summary, show)
2) importFrom(rrcov, getCenter, getEigenvalues, getLoadings, getQuan, getScores, getSdev)
* version 1.0-01
* in AllClasses.R:
1) add Fa@: communality, scoringCoef, meanF, corF
* in FaClassic.R:
1) add res@: communality, scoringCoef, meanF, corF
2) change scores to outComputeScores
* in FaCov.R:
1) add res@: communality, scoringCoef, meanF, corF
2) change scores to outComputeScores
* in factorScorePfa.R:
1) change res$correlation
2) change res$eigenvalues
3) add correlation=R
* in utils.R:
1) change the function computeScores() a lot
* computeScores.Rd:
1) revise \value{}
2) revise \examples{}