Imports: blob, DBI (>= 0.4-9), desc, hms, methods, R6, testthat (>=

person(given = "Kirill", family = "Müller", role = c("aut", "cre"), email = "krlmlr+r@mailbox.org"),

lubridate, methods, R6, rlang (>= 0.2.0), testthat (>= 2.0.0),
Suggests: debugme, devtools, knitr, lintr, rmarkdown, RSQLite
Author: Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>),

Suggests: clipr, dblog (>= 0.0.0.9008), debugme, devtools, knitr,

Description: A helper that tests DBI back ends for conformity
lubridate, methods, palmerpenguins, R6, rlang (>= 0.2.0),
testthat (>= 2.0.0), utils, withr, vctrs

Collate: 'DBItest.R' 'compat-purrr.R' 'context.R' 'dbi.R' 'dummy.R'

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

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

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

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

Title: Rank Selection for Non-Negative Matrix Factorization
Authors@R: 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"))
Description: 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.