LPsmooth: LP Smoothed Inference and Graphics

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) <doi:10.48550/arXiv.2005.13011>.

Version: 0.1.3
Imports: LPGraph, LPBkg, truncnorm, nloptr, Hmisc, orthopolynom, polynom
Published: 2020-11-24
DOI: 10.32614/CRAN.package.LPsmooth
Author: Xiangyu Zhang, Sara Algeri
Maintainer: Xiangyu Zhang <zhan6004 at umn.edu>
License: GPL-3
NeedsCompilation: no
CRAN checks: LPsmooth results


Reference manual: LPsmooth.pdf


Package source: LPsmooth_0.1.3.tar.gz
Windows binaries: r-devel: LPsmooth_0.1.3.zip, r-release: LPsmooth_0.1.3.zip, r-oldrel: LPsmooth_0.1.3.zip
macOS binaries: r-release (arm64): LPsmooth_0.1.3.tgz, r-oldrel (arm64): LPsmooth_0.1.3.tgz, r-release (x86_64): LPsmooth_0.1.3.tgz, r-oldrel (x86_64): LPsmooth_0.1.3.tgz
Old sources: LPsmooth archive


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