Package: generalCorr
Type: Package
Title: Generalized Correlations and Initial Causal Path
Version: 1.0.2
Date: 2016-04-10
Author: Prof. H. D. Vinod, Fordham University, NY.
Maintainer: H. D. Vinod <vinod@fordham.edu>
Encoding: UTF-8
Depends: R (>= 3.0.0), np (>= 0.60), xtable (>= 1.8), meboot (>= 1.4), psych (>= 1.5)
Suggests: R.rsp
VignetteBuilder: R.rsp
Description: Asymmetric generalized correlations r*(x|y) measure strength of the
dependence of x on y. If |r*(x|y)|> |r*(y|x)| it suggests that y is more likely
the "kernel cause" of x. There are at least two additional ways of comparing
two kernel regressions helping identify the `cause'.
In simultaneous equation
models where endogeneity of regressors is feared, we can use Prof. Koopmans' method
to ignore endogeneity problems when it kernel causes the dependent variable.
The usual partial correlations can be generalized for the asymmetric matrix of r*'s.
Partial correlations help asses effect of x on y after removing the effect of a
set of variables.
The package provides additional tools for causal assessment,
for printing the causal detections in a clear, comprehensive compact summary form,
for matrix algebra, for outlier detection, and for numerical integration by the
trapezoidal rule, stochastic dominance, etc.
The package has a function for bootstrap-based statistical inference and one
for a heuristic t-test.
License: GPL (>= 2)
LazyData: true
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-06-04 17:04:59 UTC; hd
Repository: CRAN
Date/Publication: 2016-06-04 19:34:56
Package: multicon
Type: Package
Title: Multivariate Constructs
Version: 1.6
Date: 2015-1-28
Author: Ryne A. Sherman
Maintainer: Ryne A. Sherman <rsherm13@fau.edu>
Description: Includes functions designed to examine relationships among multivariate constructs (e.g., personality). This includes functions for profile (within-person) analysis, dealing with large numbers of analyses, lens model analyses, and structural summary methods for data with circumplex structure. The package also includes functions for graphically comparing and displaying group means.
License: GPL-2
Depends: R (>= 3.0.0), psych, abind, foreach
Imports: mvtnorm, sciplot,
NeedsCompilation: no
Packaged: 2015-02-01 19:22:39 UTC; davidserfass
Repository: CRAN
Date/Publication: 2015-02-02 01:38:16
Package: nFactors
Type: Package
Title: Parallel Analysis and Non Graphical Solutions to the Cattell
Scree Test
Version: 2.3.3
Date: 2010-04-10
Encoding: latin1
Author: Gilles Raiche (Universite du Quebec a Montreal) and David Magis (Universite de Liege)
Maintainer: Gilles Raiche <raiche.gilles@uqam.ca>
Depends: R (>= 2.9.2), MASS, psych, boot, lattice
Suggests: xtable
Description: Indices, heuristics and strategies to help determine the number of factors/components to retain:
1. Acceleration factor (af with or without Parallel Analysis);
2. Optimal Coordinates (noc with or without Parallel Analysis);
3. Parallel analysis (components, factors and bootstrap);
4. lambda > mean(lambda) (Kaiser, CFA and related);
5. Cattell-Nelson-Gorsuch (CNG);
6. Zoski and Jurs multiple regression (b, t and p);
7. Zoski and Jurs standard error of the regression coeffcient (sescree);
8. Nelson R2;
9. Bartlett khi-2;
10. Anderson khi-2;
11. Lawley khi-2 and
12. Bentler-Yuan khi-2.
License: GPL (>= 2)
Packaged: 2011-12-19 13:42:55 UTC; HP_Propriétaire
Repository: CRAN
Date/Publication: 2011-12-20 11:47:27
Package: nonparaeff
Version: 0.5-8
Date: 2013-02-22
Title: Nonparametric Methods for Measuring Efficiency and Productivity
Author: Dong-hyun Oh <oh.donghyun77@gmail.com>, with Dukrok Suh
Maintainer: Dong-hyun Oh <oh.donghyun77@gmail.com>
Depends: R (>= 1.8.0), lpSolve, gdata, Hmisc, rms, geometry, psych, pwt
Suggests: gtools
Description: This package contains functions for measuring efficiency
and productivity of decision making units (DMUs) under the
framework of Data Envelopment Analysis (DEA) and its
variations.
License: GPL (>= 2)
URL: http://www.r-project.org
Repository: CRAN
Packaged: 2013-02-22 07:52:17 UTC; arecibo
NeedsCompilation: no
Date/Publication: 2013-02-22 09:38:43
Package: HDMD
Type: Package
Title: Statistical Analysis Tools for High Dimension Molecular Data
(HDMD)
Version: 1.2
Date: 2013-2-26
Author: Lisa McFerrin
Maintainer: Lisa McFerrin <lgmcferr@ncsu.edu>
Depends: psych, MASS
Suggests: scatterplot3d
Description: High Dimensional Molecular Data (HDMD) typically have many
more variables or dimensions than observations or replicates
(D>>N). This can cause many statistical procedures to fail,
become intractable, or produce misleading results. This
package provides several tools to reduce dimensionality and
analyze biological data for meaningful interpretation of
results. Factor Analysis (FA), Principal Components Analysis
(PCA) and Discriminant Analysis (DA) are frequently used
multivariate techniques. However, PCA methods prcomp and
princomp do not reflect the proportion of total variation of
each principal component. Loadings.variation displays the
relative and cumulative contribution of variation for each
component by accounting for all variability in data. When D>>N,
the maximum likelihood method cannot be applied in FA and the
the principal axes method must be used instead, as in factor.pa
of the psych package. The factor.pa.ginv function in this
package further allows for a singular covariance matrix by
applying a general inverse method to estimate factor scores.
Moreover, factor.pa.ginv removes and warns of any variables
that are constant, which would otherwise create an invalid
covariance matrix. Promax.only further allows users to define
rotation parameters during factor estimation. Similar to the
Euclidean distance, the Mahalanobis distance estimates the
relationship among groups. pairwise.mahalanobis computes all
such pairwise Mahalanobis distances among groups and is useful
for quantifying the separation of groups in DA. Genetic
sequences are composed of discrete alphabetic characters, which
makes estimates of variability difficult. MolecularEntropy and
MolecularMI calculate the entropy and mutual information to
estimate variability and covariability, respectively, of DNA or
Amino Acid sequences. Functional grouping of amino acids
(Atchley et al 1999) is also available for entropy and mutual
information estimation. Mutual information values can be
normalized by NMI to account for the background distribution
arising from the stochastic pairing of independent, random
sites. Alternatively, discrete alphabetic sequences can be
transformed into biologically informative metrics to be used in
various multivariate procedures. FactorTransform converts
amino acid sequences using the amino acid indices determined by
Atchley et al 2005.
License: GPL (>= 2)
Packaged: 2013-02-26 21:32:05 UTC; LisaMc
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-02-27 07:31:03