c(person("Cristian", "Preda", role = "aut", email = "cristian.preda@inria.fr"),
person("Quentin", "Grimonprez", role = c("aut", "cre"), email = "quentingrim@yahoo.fr"),
person("Vincent", "Vandewalle", role = "ctb", email = "vincent.vandewalle@inria.fr"))
Package for the analysis of categorical functional data.
The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>.
It also provides functions to perform basic statistical analysis on categorical functional data.
Cristian Preda [aut],
Quentin Grimonprez [aut, cre],
Vincent Vandewalle [ctb]

Package for the analysis of categorical functional data.
The main purpose is to compute an encoding (real functional variable) for each state <doi:10.1080/00031305.2017.1375990>.
It also provides functions to perform basic statistical analysis on categorical functional data.

Package for the analysis of categorical functional data.
The main purpose is to compute an encoding (real functional variable) for each state <https://hal.inria.fr/hal-02973094>.
It also provides functions to perform basic statistical analysis on categorical functional data.
msm, diagram, ggplot2, mgcv, parallel, pbapply

Counterfactual Decomposition: MC Integration of the G-Formula
c(
person("Maarten", "Bijlsma", role=c("aut", "cre"), email=  "maarten.bijlsma@gmail.com"),
person("Nikkil", "Sudharsanan", role=c("aut")),
person("Peng", "Li", role=c("ctb"))
)
Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.
Maarten Bijlsma [aut, cre],
Nikkil Sudharsanan [aut],
Peng Li [ctb]