Generalized Correlations, Causal Paths and Portfolio Selection
bootstrap confidence intervals for (x2-x1) exact SD1 to SD4 stochastic...
Compute vector of n999 nonlinear Granger causality paths
Compute vector of n999 nonlinear Granger causality paths
Compute matrix of n999 rows and p-1 columns of bootstrap `sum' (scores...
Compute matrix of n999 rows and p-1 columns of bootstrap `sum' (streng...
Compute matrix of n999 rows and p-1 columns of bootstrap `sum' index (...
Compute confidence intervals [quantile(s)] of indexes from bootPairs o...
Probability of unambiguously correct (+ or -) sign from bootPairs outp...
Probability of unambiguously correct (+ or -) sign from bootPairs outp...
Compute the portfolio return knowing the rank of a stock in the input ...
Absolute residuals of kernel regression of x on y.
Absolute values of gradients (apd's) of kernel regressions of x on y w...
Absolute values of gradients (apd's) of kernel regressions of x on y w...
Absolute values of residuals of kernel regressions of x on y when both...
Absolute values of residuals of kernel regressions of x on y when both...
Absolute residuals kernel regressions of standardized x on y and contr...
Absolute values of Hausman-Wu null in kernel regressions of x on y whe...
Block version of abs-stdres Absolute values of residuals of kernel reg...
Block version of Absolute values of residuals of kernel regressions of...
Block version abs_stdrhser Absolute residuals kernel regressions of st...
Report causal identification for all pairs of variables in a matrix (d...
Compute the numerical integration by the trapezoidal rule.
Compute usual summary stats of 'sum' indexes from bootPairs output
Compute usual summary stats of 'sum' index in (-100, 100) from bootPai...
Generalized canonical correlation, estimating alpha, beta, rho.
All Pair Version Kernel (block) causality summary paths from three cri...
Block Version 2: Kernel causality summary of causal paths from three c...
Kernel regressions based causal paths in Panel Data.
Kernel causality summary of evidence for causal paths from three crite...
Older Kernel causality summary of evidence for causal paths from three...
Kernel causality summary of evidence for causal paths from three crite...
No Print version Kernel causality summary of evidence for causal paths...
Block Version 2: Kernel causality summary of causal paths from three c...
No print (NoP) version of causeSummBlk summary causal paths from three...
Compute cofactor of a matrix based on row r and column c.
Compares two vectors (portfolios) using stochastic dominance of orders...
Compares two vectors (portfolios) using momentVote, DecileVote and exa...
Function compares nine deciles of stock return distributions.
depMeas Signed measure of nonlinear nonparametric dependence between t...
order 4 differencing of a time series vector
order four differencing of a matrix of time series
Exact stochastic dominance computation from areas above ECDF pillars.
Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.
Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.
Nonlinear Granger causality between two time series workhorse function...
Nonlinear Granger causality between two time series workhorse function...
generalCorr package description:
Function to compute outliers and their count using Tukey's method usin...
Two sequences: starting+ending values from n and blocksize (internal u...
Matrix R* of generalized correlation coefficients captures nonlinearit...
Matrix R* of generalized correlation coefficients captures nonlinearit...
compute the matrix R* of generalized correlation coefficients.
Function to compute generalized correlation coefficients r*(x|y) and r...
Heuristic t test of the difference between two generalized correlation...
internal object
internal ii
Kernel regression with options for residuals and gradients.
Kernel regression with control variables and optional residuals and gr...
Kernel regression version 2 with optional residuals and gradients with...
Kernel regression with control variables and optional residuals and gr...
Approximate overall magnitudes of kernel regression partials dx/dy and...
After removing control variables, magnitude of effect of x on y, and o...
Function to do compute the minor of a matrix defined by row r and colu...
Function compares Pearson Stats and Sharpe Ratio for a matrix of stock...
Function to do pairwise deletion of missing rows.
Function to do matched deletion of missing rows from x, y and z variab...
Function to do matched deletion of missing rows from x, y and control ...
Compute fitted values from kernel regression of x on y and y on x
Compare out-of-sample portfolio choice algorithms by a leave-percent-o...
Compare out-of-sample (short) selling algorithms by a leave-percent-ou...
Function to compute a vector of 2 lagged values of a variable from pan...
Function for computing a vector of one-lagged values of xj, a variable...
Generalized partial correlation coefficients between Xi and Xj, after ...
Generalized partial correlation coefficient between Xi and Xj after re...
Partial correlation coefficient between Xi and Xj after removing the l...
Compute generalized (ridge-adjusted) partial correlation coefficients ...
Block version of generalized partial correlation coefficients between ...
Block version reports many generalized partial correlation coefficient...
Generalized partial correlation coefficients between Xi and Xj, after ...
Generalized partial correlation coefficients between Xi and Xj,
Report many generalized partial correlation coefficients allowing cont...
Matrix of generalized partial correlation coefficients, always leaving...
Silently compute generalized (ridge-adjusted) partial correlation coef...
Vector of generalized partial correlation coefficients (GPCC), always ...
Vector of hybrid generalized partial correlation coefficients.
Vector of hybrid generalized partial correlation coefficients.
Compute the bootstrap probability of correct causal direction.
Create a 3D pillar chart to display (x, y, z) data coordinate surface.
Intermediate weighting function giving Non-Expected Utility theory wei...
Compute probability of positive or negative sign from bootPairs output
Compute the portfolio return knowing the rank of a stock in the input ...
Function to compute generalized correlation coefficients r*(x,y).
No-print kernel-causality unanimity score matrix with optional control...
Older kernel-causality unanimity score matrix with optional control va...
kernel causality (version 2) scores with control variables
No-print kernel causality scores with control variables Hausman-Wu Cri...
Older version, kernel causality weighted sum allowing control variable...
Block Version of silentPair2 for causality scores with control variabl...
Block Version of silentPairs for causality scores with control variabl...
Function reporting detailed kernel causality results in a 7-column mat...
Kernel causality computations admitting control variables.
Kernel causality computations admitting control variables reporting a ...
Summary magnitudes after removing control variables in several pairs w...
Function reporting kernel causality results as a 7-column matrix.(depr...
Function reporting kernel causality results as a 7-column matrix, vers...
Sort all columns of matrix x with respect to the j-th column.
Residuals of kernel regressions of x on y when both x and y are standa...
Standardize x and y vectors to achieve zero mean and unit variance.
Compute vectors measuring stochastic dominance of four orders.
Pseudo regression coefficients from generalized partial correlation co...
Peudo regression coefficients from hybrid generalized partial correlat...
Compute ranks of rows of matrix and summarize them into a choice sugge...
Replace asymmetric matrix by max of abs values of [i,j] or [j,i] eleme...
Creates input for the stochastic dominance function stochdom2
Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with 'boot' provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.