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) <arXiv:2005.13011>.

Getting started

Package details

AuthorXiangyu Zhang <zhan6004@umn.edu>, Sara Algeri <salgeri@umn.edu>
MaintainerXiangyu Zhang <zhan6004@umn.edu>
Package repositoryView on CRAN
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LPsmooth documentation built on Nov. 24, 2020, 5:07 p.m.