# LPsmooth v0.1.0

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## 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>.

## Functions in LPsmooth

 Name Description dmixtruncnorm Probability density function of a mixture of truncated normals find_h_disc Finding optimal instrumental mass function. d_hat Comparison density estimate rmixtruncnorm Random numbers generator for truncated normal mixtures CDplot CD-plot and Deviance test dmixnegbinom Probability mass function of a mixture of negative binomials rmixnegbinom Random numbers generator for negative binomial mixtures find_h_cont Finding optimal instrumental density. No Results!