Alexandre Brouste, Darel Noutsa Mieniedou, Christophe Dutang
Alexandre Brouste, Darel Noutsa Mieniedou, Christophe Dutang
<onestep@univ-lemans.fr>
Provide principally an eponymic function that numerically computes the Le Cam one-step estimator which is asymptotically efficient (see e.g. L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) and can be computed faster than the maximum likelihood estimator.

Provide principally an eponymic function that numerically computes the Le Cam one-step estimator which is asymptotically efficient (see e.g. L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) and can be computed faster than the maximum likelihood estimator for large observation samples.

In this package, we introduce an R function
one_two_sample() which can deal with one and two (normal)
samples. For one normal sample x, the function reports
descriptive statistics, plot, interval estimation and test of
hypothesis of x. For two normal samples x and y, the function
reports descriptive statistics, plot, interval estimation and
test of hypothesis of x and y, respectively. It also reports
interval estimation and test of hypothesis of mu1-mu2 (the
difference of the means of x and y) and sigma1^2 / sigma2^2
(the ratio of the variances of x and y), tests whether x and y
are from the same population, finds the correlation coefficient
of x and y if x and y have the same length.

Statistical Methods for Modeling Operational Risk
Christina Zou [aut,cre],
Marius Pfeuffer [aut],
Matthias Fischer [aut],
Kristina Dehler [ctb], Nicole Derfuss [ctb], Benedikt Graswald [ctb], Linda Moestel [ctb], Jixuan Wang [ctb], Leonie Wicht [ctb]
Functions for computing the Value-at-Risk in compound Poisson models.
The implementation comprises functions for modeling loss frequencies and loss severities with plain, mixed (Frigessi et al. (2012) <doi:10.1023/A:1024072610684>) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (Ergashev et al. (2013) <doi:10.21314/JOP.2013.131>).
In particular, the parametrization of tail distributions includes the fitting of Tukey-type distributions (Kuo and Headrick (2014) <doi:10.1155/2014/645823>). Furthermore, the package contains the modeling of bivariate dependencies between loss severities and frequencies, Monte Carlo simulation for total loss estimation as well as a closed-form approximation based on Degen (2010) <doi:10.21314/JOP.2010.084> to determine the value-at-risk.
evmix, VineCopula, tea, actuar, vcd, goftest, truncnorm,
ReIns, MASS, pracma