{ "_id": "6a20084eb401979e73446d1f", "Package": "generalCorr", "Type": "Package", "Title": "Generalized Correlations, Causal Paths and Portfolio Selection", "Version": "1.2.6", "Date": "2023-10-09", "Author": "Prof. H. D. Vinod, Fordham University, NY.", "Maintainer": "H. D. Vinod ", "Encoding": "UTF-8", "VignetteBuilder": "R.rsp", "Description": "Function gmcmtx0() computes a more reliable (general)\ncorrelation matrix. Since causal paths from data are important\nfor all sciences, the package provides many sophisticated\nfunctions. causeSummBlk() and causeSum2Blk() give\neasy-to-interpret causal paths. Let Z denote control variables\nand compare two flipped kernel regressions: X=f(Y, Z)+e1 and\nY=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then\nvariation in X is more \"exogenous or independent\" than in Y,\nand the causal path is X to Y. Criterion Cr2 requires\n|e2|<|e1|. These inequalities between many absolute values are\nquantified by four orders of stochastic dominance. Our third\ncriterion Cr3, for the causal path X to Y, requires new\ngeneralized partial correlations to satisfy |r*(x|y,z)|<\n|r*(y|x,z)|. The function parcorVec() reports generalized\npartials between the first variable and all others. The\npackage provides several R functions including get0outliers()\nfor outlier detection, bigfp() for numerical integration by the\ntrapezoidal rule, stochdom2() for stochastic dominance,\npillar3D() for 3D charts, canonRho() for generalized canonical\ncorrelations, depMeas() measures nonlinear dependence, and\ncauseSummary(mtx) reports summary of causal paths among matrix\ncolumns. Portfolio selection: decileVote(), momentVote(),\ndif4mtx(), exactSdMtx() can rank several stocks. Functions\nwhose names begin with 'boot' provide bootstrap statistical\ninference, including a new bootGcRsq() test for\n\"Granger-causality\" allowing nonlinear relations. A new tool\nfor evaluation of out-of-sample portfolio performance is\noutOFsamp(). Panel data implementation is now included. See\neight vignettes of the package for theory, examples, and usage\ntips. See Vinod (2019) \\doi{10.1080/03610918.2015.1122048}.", "License": "GPL (>= 2)", "LazyData": "true", "RoxygenNote": "7.2.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 10:51:56 UTC", "User": "root" }, "Config/pak/sysreqs": "cmake make libicu-dev", "Repository": "https://hdvinod.r-universe.dev", "Date/Publication": "2023-10-09 20:30:53 UTC", "RemoteUrl": "https://github.com/cran/generalCorr", "RemoteRef": "HEAD", "RemoteSha": "c9fa07dcd3dce61711099ee9ce2d967da9b4fa34", "MD5sum": "2f2de79da072d16836a5147bde9a5f64", "_user": "hdvinod", "_type": "src", "_file": "generalCorr_1.2.6.tar.gz", "_fileid": "f8dbd455d65cfe1e82a8e84808ba382073f0449d846f4648d1a1cc80b3755e33", "_filesize": 2934415, "_sha256": "f8dbd455d65cfe1e82a8e84808ba382073f0449d846f4648d1a1cc80b3755e33", "_created": "2026-06-03T10:51:56.000Z", "_published": "2026-06-03T10:56:14.388Z", "_distro": "noble", "_jobs": [ { "job": 79277519985, "time": 198, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "7382616923" }, { "job": 79277520033, "time": 179, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "7382609711" }, { "job": 79277519988, "time": 213, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "NOTE", "artifact": "7382609049" }, { "job": 79277520059, "time": 187, "config": "macos-release-arm64", "r": "4.6.0", "check": "NOTE", "artifact": "7382603212" }, { "job": 79276988401, "time": 189, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7382545097" }, { "job": 79277519994, "time": 118, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7382587939" }, { "job": 79277520044, "time": 168, "config": "windows-devel", "r": "4.7.0", "check": "NOTE", "artifact": "7382605684" }, { "job": 79277520001, "time": 150, "config": "windows-oldrel", "r": "4.5.3", "check": "NOTE", "artifact": "7382599703" }, { "job": 79277520069, "time": 149, "config": "windows-release", "r": "4.6.0", "check": "NOTE", "artifact": "7382599305" } ], "_buildurl": "https://github.com/r-universe/hdvinod/actions/runs/26879897808", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/generalCorr", "_commit": { "id": "c9fa07dcd3dce61711099ee9ce2d967da9b4fa34", "author": "H. 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"kernel regression gradients" ], "topics": [ "abs_stdapdC" ] }, { "page": "abs_stdres", "title": "Absolute values of residuals of kernel regressions of x on y when both x and y are standardized.", "concept": [ "kernel regression residuals" ], "topics": [ "abs_stdres" ] }, { "page": "abs_stdresC", "title": "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).", "concept": [ "kernel regression residuals" ], "topics": [ "abs_stdresC" ] }, { "page": "abs_stdrhserC", "title": "Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(RHS*y) not gradients.", "concept": [ "kernel regression residuals" ], "topics": [ "abs_stdrhserC" ] }, { "page": "abs_stdrhserr", "title": "Absolute values of Hausman-Wu null in kernel regressions of x on y when both x and y are standardized.", "concept": [ "Hausman-Wu statistic", "kernel regression" ], "topics": [ "abs_stdrhserr" ] }, { "page": "absBstdres", "title": "Block version of abs-stdres Absolute values of residuals of kernel regressions of standardized x on standardized y, no control variables.", "concept": [ "kernel regression residuals" ], "topics": [ "absBstdres" ] }, { "page": "absBstdresC", "title": "Block version of Absolute values of residuals of kernel regressions of standardized x on standardized y and control variables.", "concept": [ "kernel regression residuals" ], "topics": [ "absBstdresC" ] }, { "page": "absBstdrhserC", "title": "Block version abs_stdrhser Absolute residuals kernel regressions of standardized x on y and control variables, Cr1 has abs(Resid*RHS).", "concept": [ "kernel regression residuals" ], "topics": [ "absBstdrhserC" ] }, { "page": "allPairs", "title": "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.", "concept": [ "absolute residual values", "amorphous partial derivative apd", "stochastic dominance" ], "topics": [ "allPairs" ] }, { "page": "badCol", "title": "internal badCol", "topics": [ "badCol" ] }, { "page": "bigfp", "title": "Compute the numerical integration by the trapezoidal rule.", "concept": [ "fourth order stochastic dominance" ], "topics": [ "bigfp" ] }, { "page": "bootDom12", "title": "bootstrap confidence intervals for (x2-x1) exact SD1 to SD4 stochastic dominance .", "topics": [ "bootDom12" ] }, { "page": "bootGcLC", "title": "Compute vector of n999 nonlinear Granger causality paths", "concept": [ "maximum entropy bootstrap" ], "topics": [ "bootGcLC" ] }, { "page": "bootGcRsq", "title": "Compute vector of n999 nonlinear Granger causality paths", "concept": [ "maximum entropy bootstrap" ], "topics": [ "bootGcRsq" ] }, { "page": "bootPair2", "title": "Compute matrix of n999 rows and p-1 columns of bootstrap `sum' (scores from Cr1 to Cr3).", "concept": [ "maximum entropy bootstrap" ], "topics": [ "bootPair2" ] }, { "page": "bootPairs", "title": "Compute matrix of n999 rows and p-1 columns of bootstrap `sum' (strength from Cr1 to Cr3).", "concept": [ "maximum entropy bootstrap" ], "topics": [ "bootPairs" ] }, { "page": "bootPairs0", "title": "Compute matrix of n999 rows and p-1 columns of bootstrap `sum' index (strength from older criterion Cr1, with newer Cr2 and Cr3).", "topics": [ "bootPairs0" ] }, { "page": "bootQuantile", "title": "Compute confidence intervals [quantile(s)] of indexes from bootPairs output", "concept": [ "bootstrap confidence intervals", "kernel regression", "meboot", "pairwise comparisons" ], "topics": [ "bootQuantile" ] }, { "page": "bootSign", "title": "Probability of unambiguously correct (+ or -) sign from bootPairs output", "concept": [ "bootstrap", "kernel regression", "meboot", "pairwise comparisons" ], "topics": [ "bootSign" ] }, { "page": "bootSignPcent", "title": "Probability of unambiguously correct (+ or -) sign from bootPairs output transformed to percentages.", "concept": [ "bootstrap", "kernel regression", "meboot", "pairwise comparisons" ], "topics": [ "bootSignPcent" ] }, { "page": "bootSummary", "title": "Compute usual summary stats of 'sum' indexes from bootPairs output", "concept": [ "bootstrap", "kernel regression", "meboot", "pairwise comparisons" ], "topics": [ "bootSummary" ] }, { "page": "bootSummary2", "title": "Compute usual summary stats of 'sum' index in (-100, 100) from bootPair2", "concept": [ "bootstrap", "kernel regression", "meboot", "pairwise comparisons" ], "topics": [ "bootSummary2" ] }, { "page": "canonRho", "title": "Generalized canonical correlation, estimating alpha, beta, rho.", "topics": [ "canonRho" ] }, { "page": "causeAllPair", "title": "All Pair Version Kernel (block) causality summary paths from three criteria", "concept": [ "causal path", "stochastic dominance orders", "summary index" ], "topics": [ "causeAllPair" ] }, { "page": "causeSum2Blk", "title": "Block Version 2: Kernel causality summary of causal paths from three criteria", "concept": [ "causal path", "stochastic dominance orders", "summary index" ], "topics": [ "causeSum2Blk" ] }, { "page": "causeSum2Panel", "title": "Kernel regressions based causal paths in Panel Data.", "concept": [ "Granger causality", "stochastic dominance orders", "summary index" ], "topics": [ "causeSum2Panel" ] }, { "page": "causeSummary", "title": "Kernel causality summary of evidence for causal paths from three criteria", "concept": [ "causal path", "stochastic dominance orders", "summary index" ], "topics": [ "causeSummary" ] }, { "page": "causeSummary0", "title": "Older Kernel causality summary of evidence for causal paths from three criteria", "concept": [ "causal path", "summary index" ], "topics": [ "causeSummary0" ] }, { "page": "causeSummary2", "title": "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.", "concept": [ "causal path", "stochastic dominance orders", "summary index" ], "topics": [ "causeSummary2" ] }, { "page": "causeSummary2NoP", "title": "No Print version Kernel causality summary of evidence for causal paths from three criteria using new exact stochastic dominance.", "concept": [ "causal path", "stochastic dominance orders", "summary index" ], "topics": [ "causeSummary2NoP" ] }, { "page": "causeSummBlk", "title": "Block Version 2: Kernel causality summary of causal paths from three criteria", "concept": [ "causal path", "stochastic dominance orders", "summary index" ], "topics": [ "causeSummBlk" ] }, { "page": "causeSumNoP", "title": "No print (NoP) version of causeSummBlk summary causal paths from three criteria", "concept": [ "causal path", "stochastic dominance orders", "summary index" ], "topics": [ "causeSumNoP" ] }, { "page": "cofactor", "title": "Compute cofactor of a matrix based on row r and column c.", "concept": [ "cofactor of a matrix" ], "topics": [ "cofactor" ] }, { "page": "comp_portfo2", "title": "Compares two vectors (portfolios) using stochastic dominance of orders 1 to 4.", "concept": [ "financial portfolio choice", "stochastic dominance" ], "topics": [ "comp_portfo2" ] }, { "page": "compPortfo", "title": "Compares two vectors (portfolios) using momentVote, DecileVote and exactSdMtx functions.", "concept": [ "financial portfolio choice", "stochastic dominance" ], "topics": [ "compPortfo" ] }, { "page": "da", "title": "internal da", "topics": [ "da" ] }, { "page": "da2Lag", "title": "internal da2Lag", "topics": [ "da2Lag" ] }, { "page": "decileVote", "title": "Function compares nine deciles of stock return distributions.", "topics": [ "decileVote" ] }, { "page": "depMeas", "title": "depMeas Signed measure of nonlinear nonparametric dependence between two vectors.", "concept": [ "asymmetric p-values" ], "topics": [ "depMeas" ] }, { "page": "dif4", "title": "order 4 differencing of a time series vector", "topics": [ "dif4" ] }, { "page": "dif4mtx", "title": "order four differencing of a matrix of time series", "topics": [ "dif4mtx" ] }, { "page": "diff.e0", "title": "Internal diff.e0", "topics": [ "diff.e0" ] }, { "page": "dig", "title": "Internal dig", "topics": [ "dig" ] }, { "page": "e0", "title": "internal e0", "topics": [ "e0" ] }, { "page": "EuroCrime", "title": "European Crime Data", "topics": [ "EuroCrime" ] }, { "page": "exactSdMtx", "title": "Exact stochastic dominance computation from areas above ECDF pillars.", "topics": [ "exactSdMtx" ] }, { "page": "GcRsqX12", "title": "Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.", "topics": [ "GcRsqX12" ] }, { "page": "GcRsqX12c", "title": "Generalized Granger-Causality. If dif>0, x2 Granger-causes x1.", "topics": [ "GcRsqX12c" ] }, { "page": "GcRsqYX", "title": "Nonlinear Granger causality between two time series workhorse function.", "topics": [ "GcRsqYX" ] }, { "page": "GcRsqYXc", "title": "Nonlinear Granger causality between two time series workhorse function.(local constant version)", "topics": [ "GcRsqYXc" ] }, { "page": "generalCorrInfo", "title": "generalCorr package description:", "topics": [ "generalCorr-package", "generalCorrInfo" ] }, { "page": "get0outliers", "title": "Function to compute outliers and their count using Tukey's method using 1.5 times interquartile range (IQR) to define boundaries.", "concept": [ "outlier detection" ], "topics": [ "get0outliers" ] }, { "page": "getSeq", "title": "Two sequences: starting+ending values from n and blocksize (internal use)", "topics": [ "getSeq" ] }, { "page": "gmc0", "title": "internal gmc0", "topics": [ "gmc0" ] }, { "page": "gmc1", "title": "internal gmc1", "topics": [ "gmc1" ] }, { "page": "gmcmtx0", "title": "Matrix R* of generalized correlation coefficients captures nonlinearities.", "concept": [ "R* asymmetric matrix of generalized correlation coefficients", "kernel regression" ], "topics": [ "gmcmtx0" ] }, { "page": "gmcmtxBlk", "title": "Matrix R* of generalized correlation coefficients captures nonlinearities using blocks.", "concept": [ "R* asymmetric matrix of generalized correlation coefficients", "blocking observations", "kernel regression" ], "topics": [ "gmcmtxBlk" ] }, { "page": "gmcmtxZ", "title": "compute the matrix R* of generalized correlation coefficients.", "concept": [ "R* asymmetric correlations", "kernel regression" ], "topics": [ "gmcmtxZ" ] }, { "page": "gmcxy_np", "title": "Function to compute generalized correlation coefficients r*(x|y) and r*(y|x) from two vectors (not matrices)", "concept": [ "R* asymmetric correlations", "kernel regression" ], "topics": [ "gmcxy_np" ] }, { "page": "goodCol", "title": "internal goodCol", "topics": [ "goodCol" ] }, { "page": "heurist", "title": "Heuristic t test of the difference between two generalized correlations.", "concept": [ "paired t test" ], "topics": [ "heurist" ] }, { "page": "i", "title": "internal i", "topics": [ "i" ] }, { "page": "ibad", "title": "internal object", "topics": [ "ibad" ] }, { "page": "ii", "title": "internal ii", "topics": [ "ii" ] }, { "page": "j", "title": "internal j", "topics": [ "j" ] }, { "page": "kern", "title": "Kernel regression with options for residuals and gradients.", "concept": [ "apd amorphous partial derivative", "kernel regression gradients", "kernel regression residuals" ], "topics": [ "kern" ] }, { "page": "kern_ctrl", "title": "Kernel regression with control variables and optional residuals and gradients.", "concept": [ "apd amorphous partial derivative", "kernel regression gradients", "kernel regression residuals" ], "topics": [ "kern_ctrl" ] }, { "page": "kern2", "title": "Kernel regression version 2 with optional residuals and gradients with regtype=\"ll\" for local linear, bwmethod=\"cv.aic\" for AIC-based bandwidth selection.", "concept": [ "apd amorphous partial derivative", "kernel regression gradients", "kernel regression residuals" ], "topics": [ "kern2" ] }, { "page": "kern2ctrl", "title": "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.", "concept": [ "apd amorphous partial derivative", "kernel regression gradients", "kernel regression residuals" ], "topics": [ "kern2ctrl" ] }, { "page": "mag", "title": "Approximate overall magnitudes of kernel regression partials dx/dy and dy/dx.", "concept": [ "amorphous partial derivatives" ], "topics": [ "mag" ] }, { "page": "mag_ctrl", "title": "After removing control variables, magnitude of effect of x on y, and of y on x.", "concept": [ "apd amorphous partial derivatives" ], "topics": [ "mag_ctrl" ] }, { "page": "min.e0", "title": "internal min.e0", "topics": [ "min.e0" ] }, { "page": "minor", "title": "Function to do compute the minor of a matrix defined by row r and column c.", "concept": [ "minor of a matrix" ], "topics": [ "minor" ] }, { "page": "momentVote", "title": "Function compares Pearson Stats and Sharpe Ratio for a matrix of stock returns", "topics": [ "momentVote" ] }, { "page": "mtx", "title": "internal mtx", "topics": [ "mtx" ] }, { "page": "mtx0", "title": "internal mtx0", "topics": [ "mtx0" ] }, { "page": "mtx2", "title": "internal mtx2", "topics": [ "mtx2" ] }, { "page": "n", "title": "internal n", "topics": [ "n" ] }, { "page": "nall", "title": "internal nall", "topics": [ "nall" ] }, { "page": "nam.badCol", "title": "internal nam.badCol", "topics": [ "nam.badCol" ] }, { "page": "nam.goodCol", "title": "internal nam.goodCol", "topics": [ "nam.goodCol" ] }, { "page": "nam.mtx0", "title": "internal nam.mtx0", "topics": [ "nam.mtx0" ] }, { "page": "napair", "title": "Function to do pairwise deletion of missing rows.", "topics": [ "napair" ] }, { "page": "naTriple", "title": "Function to do matched deletion of missing rows from x, y and z variable(s).", "topics": [ "naTriple" ] }, { "page": "naTriplet", "title": "Function to do matched deletion of missing rows from x, y and control variable(s).", "topics": [ "naTriplet" ] }, { "page": "NLhat", "title": "Compute fitted values from kernel regression of x on y and y on x", "topics": [ "NLhat" ] }, { "page": "out1", "title": "internal out1", "topics": [ "out1" ] }, { "page": "outOFsamp", "title": "Compare out-of-sample portfolio choice algorithms by a leave-percent-out method.", "topics": [ "outOFsamp" ] }, { "page": "outOFsell", "title": "Compare out-of-sample (short) selling algorithms by a leave-percent-out method.", "topics": [ "outOFsell" ] }, { "page": "p1", "title": "internal p1", "topics": [ "p1" ] }, { "page": "Panel2Lag", "title": "Function to compute a vector of 2 lagged values of a variable from panel data.", "topics": [ "Panel2Lag" ] }, { "page": "PanelLag", "title": "Function for computing a vector of one-lagged values of xj, a variable from panel data.", "topics": [ "PanelLag" ] }, { "page": "parcor_ijk", "title": "Generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.", "topics": [ "parcor_ijk" ] }, { "page": "parcor_ijkOLD", "title": "Generalized partial correlation coefficient between Xi and Xj after removing the effect of all others. (older version, deprecated)", "topics": [ "parcor_ijkOLD" ] }, { "page": "parcor_linear", "title": "Partial correlation coefficient between Xi and Xj after removing the linear effect of all others.", "topics": [ "parcor_linear" ] }, { "page": "parcor_ridg", "title": "Compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*. (deprecated)", "concept": [ "partial correlations" ], "topics": [ "parcor_ridg" ] }, { "page": "parcorBijk", "title": "Block version of generalized partial correlation coefficients between Xi and Xj, after removing the effect of xk, via nonparametric regression residuals.", "topics": [ "parcorBijk" ] }, { "page": "parcorBMany", "title": "Block version reports many generalized partial correlation coefficients allowing control variables.", "concept": [ "partial correlations" ], "topics": [ "parcorBMany" ] }, { "page": "parcorHijk", "title": "Generalized partial correlation coefficients between Xi and Xj, after removing the effect of Xk, via OLS regression residuals.", "topics": [ "parcorHijk" ] }, { "page": "parcorHijk2", "title": "Generalized partial correlation coefficients between Xi and Xj,", "topics": [ "parcorHijk2" ] }, { "page": "parcorMany", "title": "Report many generalized partial correlation coefficients allowing control variables.", "concept": [ "partial correlations" ], "topics": [ "parcorMany" ] }, { "page": "parcorMtx", "title": "Matrix of generalized partial correlation coefficients, always leaving out control variables, if any.", "concept": [ "partial correlations" ], "topics": [ "parcorMtx" ] }, { "page": "parcorSilent", "title": "Silently compute generalized (ridge-adjusted) partial correlation coefficients from matrix R*.", "concept": [ "partial correlations", "ridge biasing factor" ], "topics": [ "parcorSilent" ] }, { "page": "parcorVec", "title": "Vector of generalized partial correlation coefficients (GPCC), always leaving out control variables, if any.", "concept": [ "partial correlations" ], "topics": [ "parcorVec" ] }, { "page": "parcorVecH", "title": "Vector of hybrid generalized partial correlation coefficients.", "concept": [ "partial correlations" ], "topics": [ "parcorVecH" ] }, { "page": "parcorVecH2", "title": "Vector of hybrid generalized partial correlation coefficients.", "concept": [ "partial correlations" ], "topics": [ "parcorVecH2" ] }, { "page": "pcause", "title": "Compute the bootstrap probability of correct causal direction.", "concept": [ "bootstrap", "maximum entropy bootstrap" ], "topics": [ "pcause" ] }, { "page": "pillar3D", "title": "Create a 3D pillar chart to display (x, y, z) data coordinate surface.", "concept": [ "3D plot", "wireframe plot" ], "topics": [ "pillar3D" ] }, { "page": "prelec2", "title": "Intermediate weighting function giving Non-Expected Utility theory weights.", "concept": [ "Prelec weights", "non expected utility function" ], "topics": [ "prelec2" ] }, { "page": "probSign", "title": "Compute probability of positive or negative sign from bootPairs output", "concept": [ "bootstrap", "kernel regression", "meboot", "pairwise comparisons" ], "topics": [ "probSign" ] }, { "page": "rank2return", "title": "Compute the portfolio return knowing the rank of a stock in the input `mtx'.", "topics": [ "rank2return" ] }, { "page": "rank2sell", "title": "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()'.", "topics": [ "rank2sell" ] }, { "page": "rhs.lag2", "title": "internal rhs.lag2", "topics": [ "rhs.lag2" ] }, { "page": "rhs1", "title": "internal rhs1", "topics": [ "rhs1" ] }, { "page": "ridgek", "title": "internal ridgek", "topics": [ "ridgek" ] }, { "page": "rij", "title": "internal rij", "topics": [ "rij" ] }, { "page": "rijMrji", "title": "internal rijMrji", "topics": [ "rijMrji" ] }, { "page": "rji", "title": "internal rji", "topics": [ "rji" ] }, { "page": "rrij", "title": "internal rrij", "topics": [ "rrij" ] }, { "page": "rrji", "title": "internal rrji", "topics": [ "rrji" ] }, { "page": "rstar", "title": "Function to compute generalized correlation coefficients r*(x,y).", "concept": [ "asymmetric p-values" ], "topics": [ "rstar" ] }, { "page": "sales2Lag", "title": "internal sales2Lag", "topics": [ "sales2Lag" ] }, { "page": "salesLag", "title": "internal salesLag", "topics": [ "salesLag" ] }, { "page": "seed", "title": "internal seed", "topics": [ "seed" ] }, { "page": "sgn.e0", "title": "internal sgn.e0", "topics": [ "sgn.e0" ] }, { "page": "silentMtx", "title": "No-print kernel-causality unanimity score matrix with optional control variables", "concept": [ "causal criteria", "fourth order stochastic dominance", "generalized correlations" ], "topics": [ "silentMtx" ] }, { "page": "silentMtx0", "title": "Older kernel-causality unanimity score matrix with optional control variables", "concept": [ "causal criteria", "generalized correlations" ], "topics": [ "silentMtx0" ] }, { "page": "silentPair2", "title": "kernel causality (version 2) scores with control variables", "concept": [ "Hausman-Wu exogeneity criteria", "generalized correlations", "stochastic dominance" ], "topics": [ "silentPair2" ] }, { "page": "silentPairs", "title": "No-print kernel causality scores with control variables Hausman-Wu Criterion 1", "concept": [ "Hausman-Wu exogeneity criteria", "generalized correlations", "stochastic dominance" ], "topics": [ "silentPairs" ] }, { "page": "silentPairs0", "title": "Older version, kernel causality weighted sum allowing control variables", "concept": [ "causal criteria", "generalized correlations" ], "topics": [ "silentPairs0" ] }, { "page": "siPair2Blk", "title": "Block Version of silentPair2 for causality scores with control variables", "concept": [ "Hausman-Wu exogeneity criteria", "generalized correlations", "stochastic dominance" ], "topics": [ "siPair2Blk" ] }, { "page": "siPairsBlk", "title": "Block Version of silentPairs for causality scores with control variables", "concept": [ "Hausman-Wu exogeneity criteria", "generalized correlations", "stochastic dominance" ], "topics": [ "siPairsBlk" ] }, { "page": "some0Pairs", "title": "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)", "concept": [ "causal criteria", "generalized correlations" ], "topics": [ "some0Pairs" ] }, { "page": "someCPairs", "title": "Kernel causality computations admitting control variables.", "concept": [ "causal criteria" ], "topics": [ "someCPairs" ] }, { "page": "someCPairs2", "title": "Kernel causality computations admitting control variables reporting a 7-column matrix, version 2.", "concept": [ "causal criteria", "generalized correlations", "stochastic dominance" ], "topics": [ "someCPairs2" ] }, { "page": "someMagPairs", "title": "Summary magnitudes after removing control variables in several pairs where dependent variable is fixed.", "concept": [ "partial derivatives" ], "topics": [ "someMagPairs" ] }, { "page": "somePairs", "title": "Function reporting kernel causality results as a 7-column matrix.(deprecated)", "concept": [ "causal criteria", "generalized correlations" ], "topics": [ "somePairs" ] }, { "page": "somePairs2", "title": "Function reporting kernel causality results as a 7-column matrix, version 2.", "concept": [ "causal criteria", "generalized correlations" ], "topics": [ "somePairs2" ] }, { "page": "sort_matrix", "title": "Sort all columns of matrix x with respect to the j-th column.", "topics": [ "sort_matrix" ] }, { "page": "sort.abse0", "title": "internal sort.abse0", "topics": [ "sort.abse0" ] }, { "page": "sort.e0", "title": "internal sort.e0", "topics": [ "sort.e0" ] }, { "page": "stdres", "title": "Residuals of kernel regressions of x on y when both x and y are standardized.", "concept": [ "kernel regression residuals" ], "topics": [ "stdres" ] }, { "page": "stdz_xy", "title": "Standardize x and y vectors to achieve zero mean and unit variance.", "topics": [ "stdz_xy" ] }, { "page": "stochdom2", "title": "Compute vectors measuring stochastic dominance of four orders.", "concept": [ "stochastic dominance from local kurtosis", "stochastic dominance from local skewness" ], "topics": [ "stochdom2" ] }, { "page": "sudoCoefParcor", "title": "Pseudo regression coefficients from generalized partial correlation coefficients, (GPCC).", "concept": [ "partial correlations" ], "topics": [ "sudoCoefParcor" ] }, { "page": "sudoCoefParcorH", "title": "Peudo regression coefficients from hybrid generalized partial correlation coefficients (HGPCC).", "concept": [ "partial correlations" ], "topics": [ "sudoCoefParcorH" ] }, { "page": "summaryRank", "title": "Compute ranks of rows of matrix and summarize them into a choice suggestion.", "topics": [ "summaryRank" ] }, { "page": "symmze", "title": "Replace asymmetric matrix by max of abs values of [i,j] or [j,i] elements.", "topics": [ "symmze" ] }, { "page": "wtdpapb", "title": "Creates input for the stochastic dominance function stochdom2", "concept": [ "stochastic dominance" ], "topics": [ "wtdpapb" ] } ], "_rundeps": [ "abind", "ash", "backports", "boot", "broom", "car", "carData", "cli", "colorspace", "cowplot", "cpp11", "cubature", "Deriv", "doBy", "dplyr", "dynlm", "farver", "FNN", "forecast", "Formula", "fracdiff", "generics", 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