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 |