c(person("Mark W.", "Donoghoe", email = "markdonoghoe@gmail.com", role = c("aut","cre"),
comment = c(ORCID = "0000-0003-0212-6443")),
person("Ian C.", "Marschner", role = "ths",
comment = c(ORCID = "0000-0002-6225-1572")),
person("Alexandra C.", "Gillett", role = "ctb",
comment = c("wrote an initial version of the nplbin function",
ORCID = "0000-0002-5069-3197")))
splines, glm2, turboEM (>= 2021.1), Matrix, itertools2,
iterators
Mark W. Donoghoe [aut, cre] (<https://orcid.org/0000-0003-0212-6443>),
Ian C. Marschner [ths] (<https://orcid.org/0000-0002-6225-1572>),
Alexandra C. Gillett [ctb] (wrote an initial version of the nplbin
function, <https://orcid.org/0000-0002-5069-3197>)

c(person("Mark W.", "Donoghoe", email = "markdonoghoe@gmail.com", rol = c("aut","cre")),
person("Ian C.", "Marschner", rol = "ths"),
person("Alexandra C.", "Gillett", rol = "ctb", comment = "Wrote an initial version of the nplbin function"))
Mark W. Donoghoe [aut, cre],
Ian C. Marschner [ths],
Alexandra C. Gillett [ctb] (Wrote an initial version of the nplbin
function)

CoxPH Model with Log Concave Baseline Distribution
Computes a cox PH model with a log concave baseline distribution. If no covariates are provided, estimates the log concave NPMLE. Built specifically for interval censored data, where data is a n by 2 matrix with [i, 1] as the left side of the interval for subject i and [i,2] as right side. Uncensored data can be entered by setting [i,1] = [i,2]. Alternatively, this can also handle uncensored data. If all the data is uncensored, you may enter data as a length(n) vector.

Maximum likelihood estimation of a log-concave density based on
censored data
Dominic Schuhmacher <schuhmacher@stat.unibe.ch>, Kaspar
Rufibach <kaspar.rufibach@gmail.com>, Lutz Duembgen
<duembgen@stat.unibe.ch>
R (>= 2.10.0), base, methods, utils, grDevices, graphics,
stats
Based on right or interval censored data, compute the
maximum likelihood estimator of a density under the assumption
that it is log-concave. For further information see Duembgen,
Schuhmacher, and Rufibach (2011, preprint).