Implementation of the Stochastic Expectation Maximisation (StEM) approach to Record Linkage described in the paper by K. Robach, S. L. van der Pas, M. A. van de Wiel and M. H. Hof (2024, <doi:10.1093/jrsssc/qlaf016>); see citation("FlexRL") for details. This is a record linkage method, for finding the common set of records among 2 data sources based on Partially Identifying Variables (PIVs) available in both sources. It includes modelling of dynamic Partially Identifying Variables (e.g. postal code) that may evolve over time and registration errors (missing values and mistakes in the registration). Low memory footprint.

Package for parametric relative survival analyses. It allows to model non-linear and 
        non-proportional effects and both non proportional and non linear effects, using splines (B-spline and truncated power basis), Weighted Cumulative Index of Exposure effect, with correction model for 
        the life table. Both non proportional and non linear effects are described in 
			Remontet, L. et al. (2007) <doi:10.1002/sim.2656> and 
			Mahboubi, A. et al. (2011) <doi:10.1002/sim.4208>.

An easy way to conduct flexible scan.
    Monte-Carlo method is used to test the spatial clusters given the cases, population, and shapefile.
    A table with formal style and a map with clusters are included in the result report.
    The method can be referenced at: Toshiro Tango and Kunihiko Takahashi (2005) <doi:10.1186/1476-072X-4-11>.

A drop-in replacement for 'flexdashboard' 'Rmd' documents, which
  implements an after-knit-hook to split the generated single page application
  in one document per main section to reduce rendering load in the web browser
  displaying the document. Put all 'JavaScript' stuff needed in all sections 
  before the first headline featuring navigation menu attributes. This package 
  is experimental and maybe replaced by a solution inside 'flexdashboard'.

Flexible parametric models for time-to-event data,
    including the Royston-Parmar spline model, generalized gamma and
    generalized F distributions.  Any user-defined parametric
    distribution can be fitted, given at least an R function defining
    the probability density or hazard. There are also tools for
    fitting and predicting from fully parametric multi-state models,
    based on either cause-specific hazards or mixture models.