EuroCrime               European Crime Data
GcRsqX12                Generalized Granger-Causality. If dif>0, x2
                        Granger-causes x1.
GcRsqX12c               Generalized Granger-Causality. If dif>0, x2
                        Granger-causes x1.
GcRsqYX                 Nonlinear Granger causality between two time
                        series workhorse function.
GcRsqYXc                Nonlinear Granger causality between two time
                        series workhorse function.(local constant
                        version)
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
                        (C for control presence).
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.
bootDom12               bootstrap confidence intervals for (x2-x1)
                        exact SD1 to SD4 stochastic dominance .
bootGcLC                Compute vector of n999 nonlinear Granger
                        causality paths
bootGcRsq               Compute vector of n999 nonlinear Granger
                        causality paths
bootPair2               Compute matrix of n999 rows and p-1 columns of
                        bootstrap 'sum' (scores from Cr1 to Cr3).
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
bootSummary2            Compute usual summary stats of 'sum' index in
                        (-100, 100) from bootPair2
canonRho                Generalized canonical correlation, estimating
                        alpha, beta, rho.
causeAllPair            All Pair Version Kernel (block) causality
                        summary paths from three criteria
causeSum2Blk            Block Version 2: Kernel causality summary of
                        causal paths from three criteria
causeSum2Panel          Kernel regressions based causal paths in Panel
                        Data.
causeSumNoP             No print (NoP) version of causeSummBlk summary
                        causal paths from three criteria
causeSummBlk            Block Version 2: Kernel causality summary of
                        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
causeSummary2           Kernel causality summary of evidence for causal
                        paths from three criteria using new exact
                        stochastic dominance. The function develops a
                        unanimity index for deciding which flip (y on
                        xi) or (xi on y) is best. Relevant signs
                        determine the causal direction and unanimity
                        index among three criteria. While allowing the
                        researcher to keep some variables as controls,
                        or outside the scope of causal path
                        determination (e.g., age or latitude) this
                        function produces detailed causal path
                        information in a 5 column matrix identifying
                        the names of variables, causal path directions,
                        path strengths re-scaled to be in the range
                        [-100, 100], (table reports absolute values of
                        the strength) plus Pearson correlation and its
                        p-value. The '2' in the name of the function
                        suggests a second implementation where exact
                        stochastic dominance, decileVote, and
                        momentVote are used and where we avoid
                        Anderson's trapezoidal approximation.
causeSummary2NoP        No Print version Kernel causality summary of
                        evidence for causal paths from three criteria
                        using new exact stochastic dominance.
cofactor                Compute cofactor of a matrix based on row r and
                        column c.
compPortfo              Compares two vectors (portfolios) using
                        momentVote, DecileVote and exactSdMtx
                        functions.
comp_portfo2            Compares two vectors (portfolios) using
                        stochastic dominance of orders 1 to 4.
da                      internal da
da2Lag                  internal da2Lag
decileVote              Function compares nine deciles of stock return
                        distributions.
depMeas                 depMeas Signed measure of nonlinear
                        nonparametric dependence between two vectors.
dif4                    order 4 differencing of a time series vector
dif4mtx                 order four differencing of a matrix of time
                        series
diff.e0                 Internal diff.e0
dig                     Internal dig
e0                      internal e0
exactSdMtx              Exact stochastic dominance computation from
                        areas above ECDF pillars.
generalCorrInfo         generalCorr package description:
get0outliers            Function to compute outliers and their count
                        using Tukey's 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.
kern2                   Kernel regression version 2 with optional
                        residuals and gradients with regtype="ll" for
                        local linear, bwmethod="cv.aic" for AIC-based
                        bandwidth selection.
kern2ctrl               Kernel regression with control variables and
                        optional residuals and gradients. version 2
                        regtype="ll" for local linear,
                        bwmethod="cv.aic" for AIC-based bandwidth
                        selection. It admits control variables.
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.
momentVote              Function compares Pearson Stats and Sharpe
                        Ratio for a matrix of stock returns
mtx                     internal mtx
mtx0                    internal mtx0
mtx2                    internal mtx2
n                       internal n
naTriple                Function to do matched deletion of missing rows
                        from x, y and z variable(s).
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
outOFsamp               Compare out-of-sample portfolio choice
                        algorithms by a leave-percent-out method.
outOFsell               Compare out-of-sample (short) selling
                        algorithms by a leave-percent-out method.
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.
parcorHijk              Generalized partial correlation coefficients
                        between Xi and Xj, after removing the effect of
                        Xk, via OLS regression residuals.
parcorHijk2             Generalized partial correlation coefficients
                        between Xi and Xj,
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*.
parcorVec               Vector of generalized partial correlation
                        coefficients (GPCC), always leaving out control
                        variables, if any.
parcorVecH              Vector of hybrid generalized partial
                        correlation coefficients.
parcorVecH2             Vector of hybrid generalized partial
                        correlation coefficients.
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
rank2return             Compute the portfolio return knowing the rank
                        of a stock in the input 'mtx'.
rank2sell               Compute the portfolio return knowing the rank
                        of a stock in the input 'mtx'. This function
                        computes the return earned knowing the rank of
                        a stock computed elsewhere and named myrank
                        associate with the data columns in the input
                        mtx of stock returns. For example, mtx has p=28
                        Dow Jones stocks over n=169 monthly returns.
                        Portfolio weights are assumed to be linearly
                        declining. If maxChosen=4, the weights are
                        1/10, 2/10, 3/10 and 4/10, which add up to
                        unity. These portfolio weights are assigned in
                        their order in the sense that first chosen
                        stock (choice rank =p) gets portfolio
                        weight=4/10. The function computes return from
                        the stocks using the 'myrank' argument. This
                        helps in assessing out-of-sample performance of
                        (short) the strategy of selling lowest ranking
                        stocks. It is mostly for internal use by
                        'outOFsell()'. This is a sell version of
                        'rank2return()'.
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
siPair2Blk              Block Version of silentPair2 for causality
                        scores with control variables
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
silentPair2             kernel causality (version 2) scores with
                        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.
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.
sudoCoefParcor          Pseudo regression coefficients from generalized
                        partial correlation coefficients, (GPCC).
sudoCoefParcorH         Peudo regression coefficients from hybrid
                        generalized partial correlation coefficients
                        (HGPCC).
summaryRank             Compute ranks of rows of matrix and summarize
                        them into a choice suggestion.
symmze                  Replace asymmetric matrix by max of abs values
                        of [i,j] or [j,i] elements.
wtdpapb                 Creates input for the stochastic dominance
                        function stochdom2
