Fits a non-linear transformation model (nltm) for analyzing survival data, see Tsodikov (2003) "Semiparametricmodels: a generalized self-consistency approach". J.R. Statistical Society B, 65, Part 3, 759-774. The class of nltm includes the following currently supported models: Cox proportional hazard, proportional hazard cure, proportional odds, proportional hazard - proportional hazard cure, proportional hazard - proportional odds cure, Gamma frailty, and proportional hazard - proportional odds.

Fits a non-linear transformation model (nltm) for
analyzing survival data, see Tsodikov (2003)
"Semiparametricmodels: a generalized self-consistency
approach". J.R. Statistical Society B, 65, Part 3, 759-774. The
class of nltm includes the following currently supported
models: Cox proportional hazard, proportional hazard cure,
proportional odds, proportional hazard - proportional hazard
cure, proportional hazard - proportional odds cure, Gamma
frailty, and proportional hazard - proportional odds.

Gilda Garibotti, Alexander Tsodikov, Mark Clements
Fits a non-linear transformation model ('nltm') for
analyzing survival data, see Tsodikov (2003) <doi:10.1111/1467-9868.00414>. The
class of 'nltm' includes the following currently supported
models: Cox proportional hazard, proportional hazard cure,
proportional odds, proportional hazard - proportional hazard
cure, proportional hazard - proportional odds cure, Gamma
frailty, and proportional hazard - proportional odds.

R functions for (non)linear time series analysis. A core
topic is order estimation through cross-validation.
Earlier versions of this packae have been removed as
they contained material not licensed under 'GPL'.

R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.

Network Meta-Analysis using Integrated Nested Laplace
Approximations
c(
person("Burak Kuersad", "Guenhan", email = "burak.gunhan@med.uni-goettingen.de", role = c("aut", "cre")))
Performs network meta-analysis using integrated nested Laplace approximations ('INLA'). Includes methods to assess the heterogeneity and inconsistency in the network. Contains more than ten different network meta-analysis data. Installation of R package 'INLA' is compulsory for successful usage. 'INLA' package can be obtained from <http://www.r-inla.org>. We recommend the testing version.