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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "LMDT" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'LMDT: Local Manifold Dispersion Test for Heteroskedasticity'
version: 0.1.0
identifiers:
- type: doi
  value: 10.32614/CRAN.package.LMDT
abstract: Implements the Local Manifold Dispersion Test (LMDT) for detecting complex,
  non-linear, and high-dimensional heteroskedasticity in linear regression models.
  LMDT measures the spectral smoothness (Dirichlet energy) of robust log-dispersion
  residuals over a self-tuning k-nearest neighbors graph Laplacian, as formulated
  in Jabbar (2026). The test statistic is asymptotically distribution-free with respect
  to error kurtosis and accommodates high-dimensional covariates (p > 50) via Gavish-Donoho
  optimal hard thresholding and supervised metric learning.
authors:
- family-names: Jabbar
  given-names: Ahmed Sattar
  email: ahmed.state.me@gmail.com
preferred-citation:
  type: article
  title: 'The Local Manifold Dispersion Test: A Nonparametric Graph-Spectral Approach
    for Detecting Complex and High-Dimensional Heteroskedasticity'
  authors:
  - family-names: Jabbar
    given-names: Ahmed Sattar
    email: ahmed.state.me@gmail.com
  journal: Working Paper
  year: '2026'
repository: https://amjed-droid.r-universe.dev
repository-code: https://github.com/amjed-droid/LMDT
commit: b76302d13e023f86d3b345a69ef7acb6541e4e94
url: https://amjed-droid.github.io/LMDT/
date-released: '2026-09-26'
contact:
- family-names: Jabbar
  given-names: Ahmed Sattar
  email: ahmed.state.me@gmail.com