R: Density Estimation from GROuped Summary Statistics

# Density Estimation from GROuped Summary Statistics

## Documentation for package ‘degross’ version 0.9.0

- DESCRIPTION file.

## Help Pages

ddegross | Density function based on an object resulting from the estimation procedure in degross.
degross | Density estimation from tabulated data with given frequencies and group central moments.
degross.object | Object resulting from the estimation of a density from grouped (tabulated) summary statistics
degrossData | Creates a degrossData.object from the observed tabulated frequencies and central moments.
degrossData.object | Object generated from grouped summary statistics, including tabulated frequencies and central moments of order 1 up to 4, to estimate the underlying density using 'degross'.
degross_lpost | Log-posterior (with gradient and Fisher information) for given spline parameters, small bin frequencies, tabulated sample moments and roughness penalty parameter. This function is maximized during the M-step of the EM algorithm to estimate the B-spline parameters entering the density specification.
degross_lpostBasic | Log-posterior for given spline parameters, big bin (and optional: small bin) frequencies, tabulated sample moments and roughness penalty parameter. Compared to degross_lpost, no Fisher information matrix is computed and the gradient evaluation is optional, with a resulting computational gain.
pdegross | Cumulative distribution function (cdf) based on an object resulting from the estimation procedure in degross.
plot.degross | Plot the density estimate obtained from grouped summary statistics using degross and superpose it to the observed histogram.
print.degross | Print a 'degross' object.
print.degrossData | Print a 'degrossData' object.
qdegross | Quantile function based on an object resulting from the estimation procedure in degross.
Sigma_fun | Variance-covariance of sample central moments (root-n approximation) given the vector mu with the theoretical moments of order 1 to 8. CAREFUL: the result must be divided by n (= sample size)!
simDegrossData | Simulation of grouped data and their sample moments to illustrate the degross density estimation procedure