c(
person(given = "Sandra", family = "Siegfried", role = "aut"),
person(given = "Torsten", family = "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org",
comment = c(ORCID = "0000-0001-8301-0471")))
Count transformation models featuring
parameters interpretable as discrete hazard ratios, odds ratios,
reverse-time discrete hazard ratios, or transformed expectations.
An appropriate data transformation for a count outcome and
regression coefficients are simultaneously estimated by maximising
the exact discrete log-likelihood using the computational framework
provided in package 'mlt', technical details are given in
Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
Sandra Siegfried [aut],
Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)

Count transformation models featuring
parameters interpretable as discrete hazard ratios, odds ratios,
reverse-time discrete hazard ratios, or transformed expectations.
An appropriate data transformation for a count outcome and
regression coefficients are simultaneously estimated by maximising
the exact discrete log-likelihood using the computational framework
provided in package 'mlt', technical details are given in
Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>.
TH.data, knitr, lattice, colorspace, multcomp

c(
person(given = "Sandra", family = "Siegfried", role = "aut",
comment = c(ORCID = "0000-0002-7312-1001")),
person(given = "Torsten", family = "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org",
comment = c(ORCID = "0000-0001-8301-0471")))
Sandra Siegfried [aut] (<https://orcid.org/0000-0002-7312-1001>),
Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)

c(
person(given = "Sandra", family = "Siegfried", role = "aut",
comment = c(ORCID = "0000-0002-7312-1001")),
person(given = "Luisa", family = "Barbanti", role = "aut",
comment = c(ORCID = "0000-0001-5352-5802")),
person(given = "Torsten", family = "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org",
comment = c(ORCID = "0000-0001-8301-0471")))
Count transformation models featuring
parameters interpretable as discrete hazard ratios, odds ratios,
reverse-time discrete hazard ratios, or transformed expectations.
An appropriate data transformation for a count outcome and
regression coefficients are simultaneously estimated by maximising
the exact discrete log-likelihood using the computational framework
provided in package 'mlt', technical details are given in
Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>.
The package also contains an experimental implementation of
multivariate count transformation models with an application
to multi-species distribution models.
variables (>= 1.0-2), basefun (>= 1.0-5), alabama, Matrix,
methods
TH.data, knitr, lattice, colorspace, multcomp, mvtnorm,
numDeriv
Sandra Siegfried [aut] (<https://orcid.org/0000-0002-7312-1001>),
Luisa Barbanti [aut] (<https://orcid.org/0000-0001-5352-5802>),
Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)