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# CITATION 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 "LPsmooth" in publications use:'
type: software
license: GPL-3.0-only
title: 'LPsmooth: LP Smoothed Inference and Graphics'
version: 0.1.3
doi: 10.32614/CRAN.package.LPsmooth
abstract: Classical tests of goodness-of-fit aim to validate the conformity of a postulated
  model to the data under study. In their standard formulation, however, they do not
  allow exploring how the hypothesized model deviates from the truth nor do they provide
  any insight into how the rejected model could be improved to better fit the data.
  To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit
  which naturally integrates modeling, estimation, inference and graphics. In this
  package, the deviance tests and comparison density plots are performed to conduct
  the LP smoothed inference, where the letter L denotes nonparametric methods based
  on quantiles and P stands for polynomials. Simulations methods are used to perform
  variance estimation, inference and post-selection adjustments. Algeri S. and Zhang
  X. (2020) <arXiv:2005.13011>.
authors:
- family-names: Zhang
  given-names: Xiangyu
  email: zhan6004@umn.edu
- family-names: Algeri
  given-names: Sara
  email: salgeri@umn.edu
repository: https://cranhaven.r-universe.dev
commit: 6702274f13e61d578c15f3138d191bae3a806ef9
date-released: '2026-06-11'
contact:
- family-names: Zhang
  given-names: Xiangyu
  email: zhan6004@umn.edu