Changes in version 0.1.1                        

  - Fixed some documentation issues.

                 Changes in version 0.1.0 (2025-09-30)                  

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.