dplyr, rlang (>= 0.4.0), broom, tidyr, purrr, stats

dplyr, rlang (>= 0.4.0), broom, tidyr, purrr, stats, tibble

dplyr (>= 1.0.0), rlang (>= 0.4.0), broom, tidyr, purrr,
stats, tibble

c(person("Anderson", "Georgina", , "georgina.anderson@dhsc.gov.uk", c("aut", "cre")),
person("Fox", "Sebastian", , "sebastian.fox@dhsc.gov.uk", c("ctb")),
person("Francis", "Matthew", , "", c("ctb")),
person("Fryers", "Paul", , "paul.fryers@dhsc.gov.uk", c("ctb")),
person("Clegg", "Emma", , "", c("ctb")))
broom, dplyr (>= 1.0.0), lifecycle (>= 1.0.0), purrr, rlang
(>= 0.4.0), stats, tibble, tidyr
knitr, readxl, rmarkdown, testthat, withr

broom, dplyr (>= 1.0.0), lifecycle (>= 1.0.0), purrr, rlang
(>= 0.4.0), stats, tibble, tidyselect, tidyr

Tools for Inference in the Presence of a Monotone Likelihood
Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.

Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.  Supported by grant NSF DMS 1712839.

Penalised Maximum Likelihood Estimation for Hidden Semi-Markov
Models
Provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. <arXiv:2101.09197>.