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.