Estimation of a density from grouped (tabulated) summary statistics evaluated in each of the big bins (or classes) partitioning the support of the variable. These statistics include class frequencies and central moments of order one up to four. The logdensity is modelled using a linear combination of penalised Bsplines. The multinomial loglikelihood involving the frequencies adds up to a roughness penalty based on the differences in the coefficients of neighbouring Bsplines and the log of a rootn approximation of the sampling density of the observed vector of central moments in each class. The soobtained penalized loglikelihood is maximized using the EM algorithm to get an estimate of the spline parameters and, consequently, of the variable density and related quantities such as quantiles, see Lambert, P. (2021) <arXiv:2107.03883> for details.
Package details 


Author  Philippe Lambert [aut, cre] (Université de Liège / Université catholique de Louvain (Belgium)) 
Maintainer  Philippe Lambert <p.lambert@uliege.be> 
License  GPL3 
Version  0.9.0 
Package repository  View on CRAN 
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