The nlmixr team is very proud to announce the first CRAN release of nlmixr
( https://cran.r-project.org/web/packages/nlmixr/), an open source package for
population PK and PKPD modelling. nlmixr builds on the RxODE package for
simulation of nonlinear mixed effect models using ordinary differential
equations, by implementing parameter estimation algorithms like nlme and SAEM
(with gnlmm and FOCEi under active development). nlmixr greatly expands the
utility of existing packages (like nlme) by providing an efficient and
versatile way to specify pharmacometric models and dosing scenarios, with rapid
execution due to compilation to C++.
This new release features a unified user-interface. This user-interface
provides PKPD modelers a minimalist, intuitive, expressive, domain-specific
modeling language to compactly describe the model structure & efficiently
explore the population data at hand. Below is an example of specifying the
theophline data by this domain language:
f <- function(){
ini({
lCl <- -3 # log Cl (L/hr)
lVc <- -1 # log Vc (L)
lKA <- 1 # log Ka (1/hr)
add.err <- c(0, 0.2, 1)
eta.Cl ~ 0.1 # BSV Cl
eta.Vc ~ 0.1 # BSV Vc
eta.KA ~ 0.1 # BSV Ka
})
model({
## First parameters are defined in terms of the initial estimates
## parameter names.
Cl <- exp(lCl + eta.Cl)
Vc <- exp(lVc + eta.Vc)
KA <- exp(lKA + eta.KA)
## Next, the differential equations are defined
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot-Cl/Vc*centr;
## And the concentration is then calculated
cp = centr / Vc;
## Last, nlmixr is told that the plasma concentration follows
## an additive error (estimated by the parameter add.err)
cp ~ add(add.err)
})
}
fit <- nlmixr(f, theo_sd, est="saem")
plot(fit)
Get in touch, reach out to us and discuss ways you could contribute, or try it
out for yourself, and experience the joy of exploring and modelling population
data all within R! The nlmixr team is working hard to make this community based
modeling tool even better. Our goal: Express your model in nlmixr, openly and
freely.
Wenping Wang, PhD
Executive Director, Pharmacometrics
Novartis Pharmaceuticals Corporation
One Health Plaza, East Hanover, NJ 07936-1080, USA
Phone +1 862 7789009; Fax +1 9737818265
the first CRAN release of nlmixr
Thanks for the great work to make modeling in R more usable!
Quoted reply history
> On Nov 9, 2017, at 07:42, Wang, Wenping <[email protected]> wrote:
>
> The nlmixr team is very proud to announce the first CRAN release of nlmixr
> ( https://cran.r-project.org/web/packages/nlmixr/), an open source package for
> population PK and PKPD modelling. nlmixr builds on the RxODE package for
> simulation of nonlinear mixed effect models using ordinary differential
> equations, by implementing parameter estimation algorithms like nlme and SAEM
> (with gnlmm and FOCEi under active development). nlmixr greatly expands the
> utility of existing packages (like nlme) by providing an efficient and
> versatile way to specify pharmacometric models and dosing scenarios, with
> rapid execution due to compilation to C++.
>
> This new release features a unified user-interface. This user-interface
> provides PKPD modelers a minimalist, intuitive, expressive, domain-specific
> modeling language to compactly describe the model structure & efficiently
> explore the population data at hand. Below is an example of specifying the
> theophline data by this domain language:
>
> f <- function(){
> ini({
> lCl <- -3 # log Cl (L/hr)
> lVc <- -1 # log Vc (L)
> lKA <- 1 # log Ka (1/hr)
> add.err <- c(0, 0.2, 1)
> eta.Cl ~ 0.1 # BSV Cl
> eta.Vc ~ 0.1 # BSV Vc
> eta.KA ~ 0.1 # BSV Ka
> })
> model({
> ## First parameters are defined in terms of the initial estimates
> ## parameter names.
> Cl <- exp(lCl + eta.Cl)
> Vc <- exp(lVc + eta.Vc)
> KA <- exp(lKA + eta.KA)
> ## Next, the differential equations are defined
> d/dt(depot) = -KA*depot;
> d/dt(centr) = KA*depot-Cl/Vc*centr;
> ## And the concentration is then calculated
> cp = centr / Vc;
> ## Last, nlmixr is told that the plasma concentration follows
> ## an additive error (estimated by the parameter add.err)
> cp ~ add(add.err)
> })
> }
>
> fit <- nlmixr(f, theo_sd, est="saem")
> plot(fit)
>
> Get in touch, reach out to us and discuss ways you could contribute, or try
> it out for yourself, and experience the joy of exploring and modelling
> population data all within R! The nlmixr team is working hard to make this
> community based modeling tool even better. Our goal: Express your model in
> nlmixr, openly and freely.
>
> Wenping Wang, PhD
> Executive Director, Pharmacometrics
> Novartis Pharmaceuticals Corporation
> One Health Plaza, East Hanover, NJ 07936-1080, USA
> Phone +1 862 7789009; Fax +1 9737818265
Fully agree with Bill! Well done and good job. Just uploaded it and will
definitely test it...
Regards,
MAlidi
Quoted reply history
On Thu, Nov 9, 2017 at 9:29 AM, William Denney <[email protected]
> wrote:
> Thanks for the great work to make modeling in R more usable!
>
> On Nov 9, 2017, at 07:42, Wang, Wenping <[email protected]> wrote:
>
> The nlmixr team is very proud to announce the first CRAN release of nlmixr
> ( https://cran.r-project.org/web/packages/nlmixr/), an open source package
> for population PK and PKPD modelling. nlmixr builds on the RxODE package
> for simulation of nonlinear mixed effect models using ordinary differential
> equations, by implementing parameter estimation algorithms like nlme and
> SAEM (with gnlmm and FOCEi under active development). nlmixr greatly
> expands the utility of existing packages (like nlme) by providing an
> efficient and versatile way to specify pharmacometric models and dosing
> scenarios, with rapid execution due to compilation to C++.
>
>
>
> This new release features a unified user-interface. This user-interface
> provides PKPD modelers a minimalist, intuitive, expressive, domain-specific
> modeling language to compactly describe the model structure & efficiently
> explore the population data at hand. Below is an example of specifying the
> theophline data by this domain language:
>
>
>
> f <- function(){
>
> ini({
>
> lCl <- -3 # log Cl (L/hr)
>
> lVc <- -1 # log Vc (L)
>
> lKA <- 1 # log Ka (1/hr)
>
> add.err <- c(0, 0.2, 1)
>
> eta.Cl ~ 0.1 # BSV Cl
>
> eta.Vc ~ 0.1 # BSV Vc
>
> eta.KA ~ 0.1 # BSV Ka
>
> })
>
> model({
>
> ## First parameters are defined in terms of the initial
> estimates
>
> ## parameter names.
>
> Cl <- exp(lCl + eta.Cl)
>
> Vc <- exp(lVc + eta.Vc)
>
> KA <- exp(lKA + eta.KA)
>
> ## Next, the differential equations are defined
>
> d/dt(depot) = -KA*depot;
>
> d/dt(centr) = KA*depot-Cl/Vc*centr;
>
> ## And the concentration is then calculated
>
> cp = centr / Vc;
>
> ## Last, nlmixr is told that the plasma concentration follows
>
> ## an additive error (estimated by the parameter add.err)
>
> cp ~ add(add.err)
>
> })
>
> }
>
>
>
> fit <- nlmixr(f, theo_sd, est="saem")
>
> plot(fit)
>
>
>
> Get in touch, reach out to us and discuss ways you could contribute, or
> try it out for yourself, and experience the joy of exploring and modelling
> population data all within R! The nlmixr team is working hard to make this
> community based modeling tool even better. Our goal: Express your model in
> nlmixr, openly and freely.
>
>
>
> Wenping Wang, PhD
>
> Executive Director, Pharmacometrics
>
> Novartis Pharmaceuticals Corporation
>
> One Health Plaza, East Hanover, NJ 07936-1080, USA
>
> Phone +1 862 7789009 <+1%20862-778-9009>; Fax +1 9737818265
> <+1%20973-781-8265>
>
>
--
*****************************************************
web: https://www.linkedin.com/in/malidi-ahamadi-40834b11
http://www.maths.leeds.ac.uk/~malidi/
"In the end, we will remember not the words
of our enemies, but the silence of our
friends." Martin Luther King, Jr.
***************************************************