# Generalized Correlations and Initial Causal Path

## Documentation for package ‘generalCorr’ version 1.1.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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).
bootPairs0 | Compute matrix of n999 rows and p-1 columns of bootstrap 'sum' index (strength from older criterion Cr1, with newer Cr2 and Cr3).
bootQuantile | Compute quantile(s) of indexes from bootPairs output
bootSign | Compute probability of positive or negative sign from bootPairs output
bootSummary | Compute usual summary stats of 'sum' indexes from bootPairs output
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.
probSign | Compute probability of positive or negative sign from bootPairs output
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