NEWS.md

RxODE 1.1.4

RxODE 1.1.3

RxODE 1.1.2

RxODE 1.1.1

RxODE 1.1.0

RxODE 1.0.9

The iCov in the pipeline is no longer supported because it simply is a merge with the event dataset.

This can be a breaking change depending on the code you use. Note that clinical trial simulations, resampling is likely better than trying to fill out iCov for every individual which was the prior use.

RxODE 1.0.8

RxODE 1.0.7

RxODE 1.0.6

RxODE 1.0.5

RxODE 1.0.4

Breaking changes

New features

Each of these are similar to the R lag, lead, first, last and diff. However when undefined, it returns NA

This allows NONMEM-style of calculating parameters like tad:

mod1 <-RxODE({
    KA=2.94E-01;
    CL=1.86E+01;
    V2=4.02E+01;
    Q=1.05E+01;
    V3=2.97E+02;
    Kin=1;
    Kout=1;
    EC50=200;
    C2 = centr/V2;
    C3 = peri/V3;
    d/dt(depot) =-KA*depot;
    d/dt(centr) = KA*depot - CL*C2 - Q*C2 + Q*C3;
    d/dt(peri)  =                    Q*C2 - Q*C3;
    d/dt(eff)  = Kin - Kout*(1-C2/(EC50+C2))*eff;
    if (!is.na(amt)){
        tdose <- time
    } else {
        tad <- time - tdose
    }
})

It is still simpler to use:

mod1 <-RxODE({
    KA=2.94E-01;
    CL=1.86E+01;
    V2=4.02E+01;
    Q=1.05E+01;
    V3=2.97E+02;
    Kin=1;
    Kout=1;
    EC50=200;
    C2 = centr/V2;
    C3 = peri/V3;
    d/dt(depot) =-KA*depot;
    d/dt(centr) = KA*depot - CL*C2 - Q*C2 + Q*C3;
    d/dt(peri)  =                    Q*C2 - Q*C3;
    d/dt(eff)  = Kin - Kout*(1-C2/(EC50+C2))*eff;
    tad <- time - tlast
})

If the lhs parameters haven't been defined yet, they are NA

During ODE solving, the values of these are 0, but while calculating the final output the variable is randomized at least for every output. These are:

In addition, while initializing the system, the following values are simulated and retained for each individual:

All these change the solving to single thread by default to make sure the simulation is reproducible. With high loads/difficult problems the random number generator may be on a different thread and give a different number than another computer/try.

Also please note that the clang and gcc compiler use different methods to create the more complex random numbers. Therefore MacOS random numbers will be different than Linux/Windows at this time (with the exception of uniform numbers).

These numbers are still non-correlated random numbers (based on the sitmo test) with the exception of the vandercorput distributions, so if you increase the number of threads (cores=...) the results should still be valid, though maybe harder to reproduce. The faster the random number generation, the more likely these results will be reproduced across platforms.

Engine changes

https://stackoverflow.com/questions/54056594/cran-acceptable-way-of-linking-to-openmp-some-c-code-called-from-rcpp

Bug fixes:

RxODE v0.9.2-0

RxODE v0.9.1-9

RxODE v0.9.1-8



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RxODE documentation built on March 23, 2022, 9:06 a.m.