Inference of population sizes using a binomial likelihood
and least informative discrete uniform priors.

Bayesian inference from count data using discrete uniform priors
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.

Identifies code blocks that have a high level of similarity
within a set of R files.
dplyr, purrr, tibble, magrittr, methods, stringdist (>=
0.9.5.5), lintr (>= 2.0.0), rlang

'utils.R' 'dupree.R' 'dupree_classes.R'
'dupree_data_validity.R' 'dupree_code_enumeration.R'
'dups-class.R'

Mixed Proportional Hazard Competing Risk Model
person("Simen", "Gaure", email="simen@gaure.no", role=c("aut","cre"),
comment=c(ORCID="https://orcid.org/0000-0001-7251-8747"))
Estimation of mixed proportional hazard competing risk model.
The model is described in S. Gaure et al. (2007) <doi:10.1016/j.jeconom.2007.01.015>,
M. Baker and A. Melino (2000) <doi:10.1016/S0304-4076(99)00064-0>,
J. Heckman and B. Singer (1984) <doi:10.2307/1911491>, and
B.G. Lindsay (1981) <doi:10.1007/978-94-009-8552-0_8>.
Rcpp (>= 1.0.1), stats, utils, numDeriv, nloptr, parallel,
data.table, mvtnorm

Estimation of mixed proportional hazard competing risk model with NPMLE.
The model is described in S. Gaure et al. (2007) <doi:10.1016/j.jeconom.2007.01.015>,
J. Heckman and B. Singer (1984) <doi:10.2307/1911491>, and
B.G. Lindsay (1983) <doi:10.1214/aos/1176346059>.

Estimation of piecewise constant mixed proportional hazard competing risk model with NPMLE.
The model is described in S. Gaure et al. (2007) <doi:10.1016/j.jeconom.2007.01.015>,
J. Heckman and B. Singer (1984) <doi:10.2307/1911491>, and
B.G. Lindsay (1983) <doi:10.1214/aos/1176346059>.

Discrete variations of a fractional Brownian motion
Hurst exponent estimation of a fractional Brownian motion
by using discrete variations methods in presence of outliers
and/or an additive noise

c(
person("Thore", "Egeland", email = "thore.egeland@nmbu.no",
role = c("aut", "cre")),
person("Magnus Dehli", "Vigeland", email = "m.d.vigeland@medisin.uio.no",
role = "aut", comment = c(ORCID = "0000-0002-9134-4962")))
Joint DNA-based disaster victim identification (DVI), as described in
Vigeland and Egeland (2021) <doi:10.21203/rs.3.rs-296414/v1>. Joint
Victim identification is performed by optimising the joint likelihood of all
genotyped individuals. Individual identification probabilities, conditional on
all available information, are derived from the joint solution in the form of
posterior pairing probabilities. 'dvir' is part of the 'ped suite' ecosystem
for pedigree analysis, and relies in particular on 'forrel' for computation
of likelihood ratios.
Thore Egeland [aut, cre],
Magnus Dehli Vigeland [aut] (<https://orcid.org/0000-0002-9134-4962>)