Classical tests of goodnessoffit 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 goodnessoffit 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 postselection adjustments. Algeri S. and Zhang X. (2020) <arXiv:2005.13011>.
Package details 


Author  Xiangyu Zhang <zhan6004@umn.edu>, Sara Algeri <salgeri@umn.edu> 
Maintainer  Xiangyu Zhang <zhan6004@umn.edu> 
License  GPL3 
Version  0.1.3 
Package repository  View on CRAN 
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