Pooled Mean Group Estimation of Dynamic Heterogenous Panels
c(person("Piotr", "Zientara", role="aut", email="zientara@fest.pl"),
person("Lech", "Kujawski", role=c("aut", "cre"), email = "lech.kujawski@ug.edu.pl"))
Piotr Zientara [aut],
Lech Kujawski [aut, cre]
Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.

PopED: Population (and individual) optimal Experimental Design
PopED computes optimal experimental designs for both
population studies and individual studies based on nonlinear mixed-effect models.
Often this is based on a computation of the Fisher Information Matrix (FIM).

Population (and individual) optimal Experimental Design
c(person("Andrew C.","Hooker",
email="andrew.hooker@farmbio.uu.se",
role=c("aut","cre","trl","cph")),
person("Sebastian","Ueckert",
role="aut",
comment="MATLAB version"),
person("Marco","Foracchia",
role="aut",
comment="O-Matrix version"),
person("Joakim", "Nyberg",
role="aut",
comment="MATLAB version"),
person("Eric","Stroemberg",
role="ctb",
comment="MATLAB version")
)
Andrew C. Hooker [aut, cre, trl, cph],
Sebastian Ueckert [aut] (MATLAB version),
Marco Foracchia [aut] (O-Matrix version),
Joakim Nyberg [aut] (MATLAB version),
Eric Stroemberg [ctb] (MATLAB version)

PopED computes optimal experimental designs for both population
studies and individual studies based on nonlinear mixed-effect models.
Often this is based on a computation of the Fisher Information Matrix
(FIM).

Population (and Individual) Optimal Experimental Design
c(
person("Andrew C.","Hooker", email="andrew.hooker@farmbio.uu.se", role=c("aut","cre","trl","cph")),
person("Sebastian","Ueckert", role="aut", comment="MATLAB version"),
person("Marco","Foracchia", role="aut", comment="O-Matrix version"),
person("Joakim", "Nyberg", role="aut", comment="MATLAB version"),
person("Eric","Stroemberg", role="ctb", comment="MATLAB version")
)
Optimal experimental designs for both population and individual
studies based on nonlinear mixed-effect models.  Often this is based on a
computation of the Fisher Information Matrix (FIM). This package was developed
for pharmacometric problems, and examples and predefined models are available
for these types of systems.