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generalCorr

1.2.6

Generalized Correlations, Causal Paths and Portfolio Selection

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Overview

About
Maintained by H. D. VinodFirst published 2016-05-0226 releasesCRAN page ↗

Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with 'boot' provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.

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Slowest check: 4.7 min · r-oldrel-windows-x86_64
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Tests · ratio 0.00
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Documentation · exports
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Check history
  • NOTE2026-06-09
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Documentation

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READMENoVignettesNopkgdown siteNoNEWSYes · 67% structuredCode of conductNoContributing guideNo
Examples that run
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Documented parameters
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Return-value docs
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References docs
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Dependencies

Declared dependencies
5 external dependencies (excludes base and recommended)
Depends (6)
R >= 3.0.0npxtablemebootpsychlattice
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none
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Listed in earlier versions (3)
no longer listed · 1.0.0 to 1.2.6
no longer listed · 1.0.0 to 1.2.6
no longer listed · 1.0.0 to 1.2.6
Package Timeline

26 releases. Pick two to compare their code metrics. R releases are shown for context.

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    R 4.6.0 released · 2026-04-24
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    R 4.5.0 released · 2025-04-11
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    R 4.4.0 released · 2024-04-24
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    R 4.3.0 released · 2023-04-21
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    R 4.1.0 released · 2021-05-18
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  • 1.1.6
    2020-11-21 · diff ↗
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    R 4.0.0 released · 2020-04-24
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    2019-10-30 · diff ↗
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    2019-10-07 · diff ↗
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    R 3.6.0 released · 2019-04-26
  • 1.1.2
    2018-07-12 · diff ↗
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    R 3.5.0 released · 2018-04-23
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    2018-01-24 · diff ↗
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    2018-01-05 · diff ↗
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    2017-10-04 · diff ↗
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    2017-09-11 · diff ↗
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    2017-07-22 · diff ↗
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    2017-06-16 · diff ↗
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    2017-06-08 · diff ↗
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    R 3.4.0 released · 2017-04-21
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    2016-06-28 · diff ↗
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    2016-06-04 · diff ↗
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    2016-05-21 · diff ↗
  • R
    R 3.3.0 released · 2016-05-03
  • 1.0.0
    2016-05-02
  • R
    R 3.2.0 released · 2015-04-16

Package metadata

First published
2016-05-02
Total releases
26 / 10 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 3.0.0
Bundled data
91 KB / 37 files
Download size
2.3 MB
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not tracked yet
With dependencies
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APA

Balamuta, J. J. (2026). R Observatory: Metrics for generalCorr version 1.2.6 [Data set]. HJJB, LLC. Data release v2026-08-24. https://doi.org/10.5281/zenodo.21843040

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