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nonParQuantileCausality 0.1.0 (2025-09-15)

First public release (prepared for CRAN).

New features

• Introduces np_quantile_causality() — a nonparametric causality-in-quantiles test for first-order lags, supporting causality in mean and variance.
• Returns an S3 object of class np_quantile_causality with fields for statistics, quantiles, bandwidth, type, and sample size.
• Adds plot() method for np_quantile_causality objects to visualize test statistics across quantiles with a reference critical-value line.

API changes

• Renames legacy lrq.causality.test → np_quantile_causality.
• Replaces dots with underscores in all function names.
• Deprecation shim: lrq_causality_test() calls np_quantile_causality() and warns.
• Replaces do.causality.figure() with the S3 plotting interface plot.np_quantile_causality().

Data

• Bundles example dataset gold_oil (Gold, Oil) for runnable examples and tests.

Implementation details

• Bandwidth: uses KernSmooth::dpill() as a mean-regression proxy (Yu & Jones, 1998) with quantile-specific rescaling.
• Internal local-linear quantile regression helper: lprq2_() (quantreg-backed).
• Kernel matrix uses a product Gaussian kernel with relative scaling between lags.

Bug fixes

• Corrects a historical bug where x2 lags were mistakenly embedded from y2 in the variance case. Now uses embed(x2, 2) as intended.

Documentation

• Adds package-level documentation and function docs via roxygen2.
• Includes a “References” section citing:
  • Balcilar, M., Gupta, R., & Pierdzioch, C. (2016), Resources Policy, 49, 74–80.
  • Balcilar, M., Gupta, R., Kyei, C., & Wohar, M. E. (2016), Open Economies Review, 27(2), 229–250.
• Provides inst/CITATION entries for standard package citation.
• Examples demonstrate mean/variance tests and plotting using gold_oil.

Testing

• testthat suite covers:
  • Object creation and basic structure for mean/variance runs.
  • Plot method returns a ggplot object (skipped on CRAN).
• Examples and tests are lightweight and CRAN-friendly (no network or disk writes).

Licensing

• MIT license (License: MIT + file LICENSE).

Known limitations

• Current implementation supports first-order lags only.
• No built-in bootstrap wrapper for small-sample critical values.
• O(n²) kernel matrix construction may be slow for very large n.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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