| generalCorrInfo-package | generalCorr package description: |
| abs_res | Absolute residuals of kernel regression of x on y. |
| abs_stdapd | Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized. |
| abs_stdapdC | Absolute values of gradients (apd's) of kernel regressions of x on y when both x and y are standardized and control variables are present. |
| abs_stdres | Absolute values of residuals of kernel regressions of x on y when both x and y are standardized. |
| abs_stdresC | Absolute values of residuals of kernel regressions of x on y when both x and y are standardized and control variables are present. |
| allPairs | Report causal identification for all pairs of variables in a matrix. |
| badCol | internal badCol |
| bigfp | Compute the numerical integration by the trapezoidal rule. |
| bootPairs | Compute the bootstrap 'sum' of all scores using Cr1 to Cr3. |
| cofactor | Compute cofactor of a matrix based on row r and column c. |
| comp_portfo2 | Compares two vectors (portfolios) using stochastic dominance of orders 1 to 4. |
| da | internal da |
| da2Lag | internal da2Lag |
| diff.e0 | Internal diff.e0 |
| dig | Internal dig |
| e0 | internal e0 |
| EuroCrime | European Crime Data |
| generalCorrInfo | generalCorr package description: |
| get0outliers | Function to compute outliers and their count using Tukey method using 1.5 times interquartile range (IQR) to define boundarirs. |
| gmc0 | internal gmc0 |
| gmc1 | internal gmc1 |
| gmcmtx0 | Compute the matrix R* of generalized correlation coefficients. |
| gmcmtxZ | compute the matrix R* of generalized correlation coefficients. |
| gmcxy_np | Function to compute generalized correlation coefficients r*(x|y) and r*(y|x). |
| goodCol | internal goodCol |
| heurist | Heuristic t test of the difference between two generalized correlations. |
| i | internal i |
| ibad | internal object |
| ii | internal ii |
| j | internal j |
| kern | Kernel regression with options for residuals and gradients. |
| kern_ctrl | Kernel regression with control variables and optional residuals and gradients. |
| mag | Approximate overall magnitudes of kernel regression partials dx/dy and dy/dx. |
| mag_ctrl | After removing control variables, magnitude of effect of x on y, and of y on x. |
| min.e0 | internal min.e0 |
| minor | Function to do compute the minor of a matrix defined by row r and column c. |
| mtx | internal mtx |
| mtx0 | internal mtx0 |
| mtx2 | internal mtx2 |
| n | internal n |
| nall | internal nall |
| nam.badCol | internal nam.badCol |
| nam.goodCol | internal nam.goodCol |
| nam.mtx0 | internal nam.mtx0 |
| napair | Function to do pairwise deletion of missing rows. |
| naTriplet | Function to do matdched deletion of missing rows from x, y and control variable(s). |
| out1 | internal out1 |
| p1 | internal p1 |
| Panel2Lag | Function to compute a vector of 2 lagged values of a variable from panel data. |
| PanelLag | Function for computing a vector of one-lagged values of xj, a variable from panel data. |
| parcorSilent | Silently compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*. |
| parcor_ijk | Generalized partial correlation coefficient between Xi and Xj after removing the effect of all others. |
| parcor_ridg | Compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*. |
| pcause | Compute the bootstrap probability of correct causal direction. |
| prelec2 | Intermediate weighting function giving Non-Expected Utility theory weights. |
| rhs.lag2 | internal rhs.lag2 |
| rhs1 | internal rhs1 |
| ridgek | internal ridgek |
| rij | internal rij |
| rijMrji | internal rijMrji |
| rji | internal rji |
| rrij | internal rrij |
| rrji | internal rrji |
| rstar | Function to compute generalized correlation coefficients r*(x,y). |
| sales2Lag | internal sales2Lag |
| salesLag | internal salesLag |
| seed | internal seed |
| sgn.e0 | internal sgn.e0 |
| silentPairs | Function for kernel causality into 3-column matrix admitting control variables |
| some0Pairs | Function reporting kernel causality results as a detailed 7-column matrix |
| someCPairs | Function for kernel causality in 7-column matrix admitting control variables |
| someMagPairs | Summary magnitudes after removing control variables in several pairs where dependent variable is fixed. |
| somePairs | Function reporting kernel causality results as a 7-column matrix. |
| sort.abse0 | internal sort.abse0 |
| sort.e0 | internal sort.e0 |
| sort_matrix | Sort all columns of matrix x with respect to the j-th column. |
| stdz_xy | Standardize x and y vectors to achieve zero mean and unit variance. |
| stochdom2 | Compute vectors measuring stochastic dominance of four orders. |
| wtdpapb | Creates input for the function stochdom2 for stochastic dominance. |