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: DepthProc
Version: 1.0.7
Date: 2016-02-10
Title: Statistical Depth Functions for Multivariate Analysis
Author: Daniel Kosiorowski, Mateusz Bocian, Anna Wegrzynkiewicz and Zygmunt
Zawadzki from Cracow University of Economics.
Maintainer: Zygmunt Zawadzki <zawadzkizygmunt@gmail.com>
Description: Data depth concept offers a variety of powerful and user friendly tools for robust exploration and inference for multivariate data. The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nonparametric nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions.
License: GPL-2
Depends: R (>= 3.0.0), ggplot2, Rcpp (>= 0.11.2), rrcov, methods, MASS, np
Imports: lattice, sm, geometry, colorspace,
Suggests: mvtnorm, rgl, sn, robustbase, dplyr, RcppArmadillo
LinkingTo: Rcpp, RcppArmadillo
Repository: CRAN
Repository/R-Forge/Project: depthproc
Repository/R-Forge/Revision: 71
Repository/R-Forge/DateTimeStamp: 2016-02-11 20:18:06
Date/Publication: 2016-02-12 10:59:09
NeedsCompilation: yes
Packaged: 2016-02-11 20:25:29 UTC; rforge
Package: CovSel
Version: 1.2.1
Author: Jenny Häggström, Emma Persson,
Maintainer: Jenny Häggström <jenny.haggstrom@umu.se>
Depends: dr, np, MASS
Suggests: bindata
Title: Model-Free Covariate Selection
Description: Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2015-11-09 13:07:17 UTC; jennyhaggstrom
Repository: CRAN
Date/Publication: 2015-11-09 17:23:10
Package: semsfa
Type: Package
Title: Semiparametric Estimation of Stochastic Frontier Models
Version: 1.0
Date: 2015-02-18
Author: Giancarlo Ferrara and Francesco Vidoli
Maintainer: Giancarlo Ferrara <giancarlo.ferrara@gmail.com>
Description: Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.
Depends: R (>= 3.1.2), mgcv, np,
Imports: moments, doParallel, foreach, iterators
License: GPL
Packaged: 2015-02-18 08:13:25 UTC; gferrara
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-02-18 11:36:02
providing 
.