• Latest release: 0.5.12
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3 primary options exist to install mrgsolve:

• Installation from source the source tar.gz file and all dependencies, as documented in the wiki

• Latest stable version directly from github via:

devtools::install_github("metrumresearchgroup/mrgsolve@v0.5.12", subdir="rdev")

• The development version from github via:

devtools::install_github("metrumresearchgroup/mrgsolve", subdir="rdev")

Note that installation from github will automatically install all dependencies, such RcppArmadillo.

mrgsolve facilitates simulation in R from hierarchical, ordinary differential equation (ODE) based models typically employed in drug development. The modeler creates a model specification file consisting of R and C++ code that is parsed, compiled, and dynamically loaded into the R session. Input data are passed in and simulated data are returned as R objects, so disk access is never required during the simulation cycle after compiling.

• Installs via R package compile and install system
• Easy integration with any other relevant R functionality
  • Graphics: lattice, ggplot2
  • Model estimation: nls,nlme, MCMCpack, saemix, others
  • Optimal design: PFIM, PopED
  • Data summary: dplyr and many other functions and packages
  • Interactive model exploration with shiny

library(mrgsolve)
library(dplyr)
library(ggplot2)

code <- '
$GLOBAL
#define CP (CENT/VC)
#define INH (CP/(IC50+CP))

$SET delta=0.1

$PARAM TVCL=1, TVVC=20, KA = 1.3, KIN=100, KOUT=2, IC50=10

$CMT GUT, CENT, RESP

$MAIN
  double CL = exp(log(TVCL) + ETA(1));
  double VC = exp(log(TVVC) + ETA(2));

  RESP_0 = KIN/KOUT;

$OMEGA 0 0

$ODE
  dxdt_GUT = -KA*GUT;
  dxdt_CENT = KA*GUT - (CL/VC)*CENT;
  dxdt_RESP = KIN*(1-INH) - KOUT*RESP;

$TABLE capture(CP);
'

The model is parsed, compiled, and dynamically loaded into the R session

• Information about the model is saved as an R object
• Important model attributes can be updated in R without recompiling

mod <- mread("demo", tempdir(), code)

• Simulated data are returned as R objects
• Input and output data are kept in memory in the R process; writing or reading to disk is never necessary (unless results are to be saved for later use).

out <- mod %>%
  ev(amt=100, ii=24, addl=2) %>%
  mrgsim(end=120)

out

. Model:  demo.cpp 
. Dim:    1202 x 6 
. Time:   0 to 120 
. ID:     1 
.      ID time    GUT  CENT  RESP    CP
. [1,]  1  0.0   0.00  0.00 50.00 0.000
. [2,]  1  0.0 100.00  0.00 50.00 0.000
. [3,]  1  0.1  87.81 12.16 49.72 0.608
. [4,]  1  0.2  77.11 22.78 49.03 1.139
. [5,]  1  0.3  67.71 32.04 48.11 1.602
. [6,]  1  0.4  59.45 40.11 47.06 2.006
. [7,]  1  0.5  52.20 47.14 45.96 2.357
. [8,]  1  0.6  45.84 53.25 44.87 2.663

plot(out, CP+RESP~.)

out <- mod %>%
  ev(amt=100, ii=24, addl=2) %>%
  Req(CP,RESP) %>%
  knobs(TVVC=c(10,20,40), TVCL=c(0.5,1.5))

plot(out, auto.key=list(columns=3))

mod <- mod %>% omat(cmat(0.1, 0.67, 0.4))

• Data set format that is likely familiar to modeling and simulation scientists
• No need to include observation records; mrgsolve will automatically insert

data <- expand.ev(ID=1:10, amt=c(100,300,1000)) %>%
  mutate(dose=amt)

head(data)

.   ID amt evid cmt time dose
. 1  1 100    1   1    0  100
. 2  2 100    1   1    0  100
. 3  3 100    1   1    0  100
. 4  4 100    1   1    0  100
. 5  5 100    1   1    0  100
. 6  6 100    1   1    0  100

• Pass many different data sets or implement different designs in the same model code without recompiling
• Control simulation output from R to better manage memory

out <- mod %>%
  data_set(data) %>%
  Req(RESP,CP) %>% obsonly %>%
  carry.out(evid,amt,dose) %>%
  mrgsim(end=48, seed=1010)

plot(out, RESP~time|factor(dose), scales="same")

Summarise with dplyr

out %>%
  as.tbl %>%
  group_by(dose) %>%
  summarise(rmin = min(RESP), tmim=time[which.min(RESP)])

. Source: local data frame [3 x 3]
. 
.    dose      rmin  tmim
.   (dbl)     (dbl) (dbl)
. 1   100 18.958869   2.9
. 2   300 16.117261   3.5
. 3  1000  6.198648   3.5

Plot with ggplot2

out %>%
  as.tbl %>%
  ggplot(data=.) +
  geom_line(aes(x=time, y=RESP, group=ID, col=factor(dose)))