EuroCrime               European Crime Data
NLhat                   Compute fitted values from kernel regression of
                        x on y and y on x
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.
absBstdres              Block version of abs-stdres Absolute values of
                        residuals of kernel regressions of standardized
                        x on standardized y, no control variables.
absBstdresC             Block version of Absolute values of residuals
                        of kernel regressions of standardized x on
                        standardized y and control variables.
absBstdrhserC           Block version abs_stdrhser Absolute residuals
                        kernel regressions of standardized x on y and
                        control variables, Cr1 has abs(Resid*RHS).
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) not gradients.
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 (deprecated function). It
                        is better to choose a target variable and pair
                        it with all others, instead of considering all
                        possible targets.
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 confidence intervals [quantile(s)] of
                        indexes from bootPairs output
bootSign                Probability of unambiguously correct (+ or -)
                        sign from bootPairs output
bootSignPcent           Probability of unambiguously correct (+ or -)
                        sign from bootPairs output transformed to
                        percentages.
bootSummary             Compute usual summary stats of 'sum' indexes
                        from bootPairs output
causeSummBlk            Block Version Kernel causality summary causal
                        paths from three criteria
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
depMeas                 depMeas Measure dependence between two vectors.
diff.e0                 Internal diff.e0
dig                     Internal dig
e0                      internal e0
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.
getSeq                  Two sequences: starting+ending values from n
                        and blocksize (internal use)
gmc0                    internal gmc0
gmc1                    internal gmc1
gmcmtx0                 Matrix R* of generalized correlation
                        coefficients captures nonlinearities.
gmcmtxBlk               Matrix R* of generalized correlation
                        coefficients captures nonlinearities using
                        blocks.
gmcmtxZ                 compute the matrix R* of generalized
                        correlation coefficients.
gmcxy_np                Function to compute generalized correlation
                        coefficients r*(x|y) and r*(y|x) from two
                        vectors (not matrices)
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
naTriplet               Function to do matched deletion of missing rows
                        from x, y and control variable(s).
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.
out1                    internal out1
p1                      internal p1
parcorBMany             Block version reports many generalized partial
                        correlation coefficients allowing control
                        variables.
parcorBijk              Block version of generalized partial
                        correlation coefficients between Xi and Xj,
                        after removing the effect of xk, via
                        nonparametric regression residuals.
parcorMany              Report many generalized partial correlation
                        coefficients allowing control variables.
parcorMtx               Matrix of generalized partial correlation
                        coefficients, always leaving out control
                        variables, if any.
parcorSilent            Silently compute generalized (ridge-adjusted)
                        partial correlation coefficients from matrix
                        R*.
parcor_ijk              Generalized partial correlation coefficients
                        between Xi and Xj, after removing the effect of
                        xk, via nonparametric regression residuals.
parcor_ijkOLD           Generalized partial correlation coefficient
                        between Xi and Xj after removing the effect of
                        all others. (older version, deprecated)
parcor_linear           Partial correlation coefficient between Xi and
                        Xj after removing the linear effect of all
                        others.
parcor_ridg             Compute generalized (ridge-adjusted) partial
                        correlation coefficients from matrix R*.
                        (deprecated)
pcause                  Compute the bootstrap probability of correct
                        causal direction.
pillar3D                Create a 3D pillar chart to display (x, y, z)
                        data coordinate surface.
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
siPairsBlk              Block Version of silentPairs for causality
                        scores with control variables
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 detailed kernel causality
                        results in a 7-column matrix (uses deprecated
                        criterion 1, no longer recommended but may be
                        useful for second and third criterion typ=2,3)
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, version
                        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.(deprecated)
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
