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mhpfilter 0.1.0

Initial CRAN Release (2026-02-06)

This is the first official release of mhpfilter to CRAN.

Overview

mhpfilter implements the Modified Hodrick-Prescott (HP) Filter for decomposing macroeconomic time series into trend and cyclical components. The package automatically selects the optimal smoothing parameter λ using generalized cross-validation (GCV), based on the methodology of Choudhary, Hanif & Iqbal (2014).

Core Features

Filtering Functions

• mhp_filter() - Modified HP filter with automatic λ selection via GCV
• hp_filter() - Standard HP filter with fixed λ parameter
• mhp_compare() - Compare HP vs Modified HP for single series
• mhp_batch() - Batch processing for multiple time series
• batch_compare() - Batch comparison of HP vs Modified HP

Visualization Functions

• autoplot.mhp() - ggplot2-based visualization for mhp objects
• plot_comparison() - Visual comparison of HP vs Modified HP results
• plot_batch() - Visualization of batch processing results

Utility Functions

• get_lambda() - Extract optimal λ from results
• get_gcv() - Extract GCV value from results
• print.mhp() - Print method for mhp class objects

Technical Implementation

High Performance

• Fast C++ implementation using RcppArmadillo for matrix operations
• Optimized grid search algorithm for λ selection
• Efficient memory management for large time series
• Linear scaling for batch processing of multiple series

Modern R Integration

• Full compatibility with data.table (≥ 1.14.0)
• Integration with collapse (≥ 2.0.0) for fast statistics
• Support for kit (≥ 0.0.11) utilities
• Works seamlessly with tidyverse (≥ 2.0.0) and fastverse (≥ 0.3.0)
• ggplot2-based professional visualizations

Documentation

Comprehensive Vignettes

• Introduction - Quick start guide with practical examples
  • Basic usage and workflow
  • Comparison with standard HP filter
  • Real-world applications
• Methodology - Mathematical theory and derivations
  • Standard HP filter formulation
  • Generalized cross-validation criterion
  • Optimal λ selection procedure
  • Performance characteristics

Function Documentation

• Complete roxygen2 documentation for all functions
• Mathematical formulas properly rendered with LaTeX
• Extensive examples with commentary
• Author affiliations and ORCID identifiers

Package Website

• Professional pkgdown site with full documentation
• Download statistics and badges
• Interactive examples and tutorials
• API reference with search functionality

Methodology Reference

This implementation is based on:

Choudhary, M.A., Hanif, M.N., & Iqbal, J. (2014). “On smoothing macroeconomic time series using the modified HP filter.” Applied Economics, 46(19), 2205-2214. https://doi.org/10.1080/00036846.2014.896982

Authors and Contributors

Package Author and Maintainer

Muhammad Yaseen
Postdoctoral Research Fellow
School of Mathematical and Statistical Sciences
Clemson University

Original Methodology Authors

Javed Iqbal - State Bank of Pakistan
M. Nadim Hanif - State Bank of Pakistan

Installation

# From CRAN (recommended)
install.packages("mhpfilter")

# From GitHub (development version)
devtools::install_github("myaseen208/mhpfilter")

System Requirements

• R ≥ 4.0.0
• C++ compiler for building from source
• Operating systems: Windows, macOS, Linux

Testing and Quality Assurance

• Comprehensive test suite with testthat (≥ 3.0.0)
• Continuous integration via GitHub Actions
• R CMD check passes on all platforms
• CRAN policy compliance verified
• No NOTEs, WARNINGs, or ERRORs

License

MIT License

Bug Reports and Issues

Please report bugs at: https://github.com/myaseen208/mhpfilter/issues

Acknowledgments

• Original methodology: Choudhary, Hanif & Iqbal (2014)
• RcppArmadillo team for excellent C++ integration
• R Core Team for the R statistical computing environment
• CRAN team for review and hosting

For more information, visit: https://github.com/myaseen208/mhpfilter

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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