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. |
abs_stdrhserC |
Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(RHS*y) |
abs_stdrhserr |
Absolute values of Hausman-Wu null in kernel regressions of x on y when both x and y are standardized. |
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 matrix of n999 rows and p-1 columns of bootstrap 'sum' (strength from Cr1 to Cr3). |
causeSummary |
Kernel causality summary of evidence for causal paths from three criteria |
causeSummary0 |
Older Kernel causality summary of evidence for causal paths from three criteria |
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 boundaries. |
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 matched 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. |
parcorMany |
Report many generalized partial correlation coefficients allowing control variables. |
parcor_ijk |
Generalized partial correlation coefficients between Xi and Xj after removing the effect of xk. |
parcor_linear |
Partial correlation coefficient between Xi and Xj after removing the linear effect of all others. |
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 |
silentMtx |
No-print kernel-causality unanimity score matrix with optional control variables |
silentMtx0 |
Older kernel-causality unanimity score matrix with optional control variables |
silentPairs |
No-print kernel causality scores with control variables Hausman-Wu Criterion 1 |
silentPairs0 |
Older version, kernel causality weighted sum allowing control variables |
some0Pairs |
Function reporting kernel causality results as a detailed 7-column matrix |
someCPairs |
Kernel causality computations admitting control variables reporting a 7-column matrix (has older Cr1) |
someCPairs2 |
Kernel causality computations admitting control variables reporting a 7-column matrix, ver. 2 |
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. |
somePairs2 |
Function reporting kernel causality results as a 7-column matrix, version 2. |
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. |
stdres |
Residuals of kernel regressions of x on y when both x and y are standardized. |
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 stochastic dominance function stochdom2 |