Discrete Beta Kernel Graduation of mortality data
This package allows for nonparametric graduation of
mortality rates using fixed or adaptive discrete beta kernel
estimator.

This package allows for nonparametric graduation of mortality rates using fixed or adaptive discrete beta kernel estimator.

Discrete Beta Kernel Graduation of Mortality Data
c(person(given = "Angelo", family = "Mazza", role = c("aut", "cre"), email = "a.mazza@unict.it"),
person(given = "Antonio", family = "Punzo", role = "aut"))
Angelo Mazza [aut, cre],
Antonio Punzo [aut]

Allows for nonparametric graduation of mortality rates using fixed or adaptive discrete beta kernel estimator.
minpack.lm, SDD, TSA, lattice, stats, graphics, grDevices

Rank Selection for Non-Negative Matrix Factorization
c(person(given = "Yun",family = "Cai",role = c("aut", "cre"),email = "Yun.Cai@dal.ca"),
person(given = "Hong",family = "Gu",role = c("aut"),email = "hgu@dal.ca"),
person(given = "Tobias",family = "Kenney",role = c("aut"),email = "tb432381@dal.ca"))
Given the non-negative data and its distribution, the package estimates the rank parameter for Non-negative Matrix Factorization. The method is based on hypothesis testing, using a deconvolved bootstrap distribution to assess the significance level accurately despite the large amount of optimization error. The distribution of the non-negative data can be either Normal distributed or Poisson distributed.
Yun Cai [aut, cre],
Hong Gu [aut],
Tobias Kenney [aut]

c(person("Alireza", "Mahani", email = "alireza.s.mahani@gmail.com", role = c("cre", "aut"))
, person("Mansour", "Sharabiani", role = "aut"))
Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses.
Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler
(Neal (2003) <DOI:10.1214/aos/1056562461>), as implemented in the R package MfUSampler
(Mahani and Sharabiani (2017) <DOI: 10.18637/jss.v078.c01>).
Alireza Mahani [cre, aut],
Mansour Sharabiani [aut]