NEWS.md

rxode2 (development version)

rxode2 3.0.3

rxode2 3.0.2

rxode2 3.0.1

rxode2 3.0.0

Breaking Changes

Since the protection for divide by zero has changed, the results will also change. This is a more conservative protection mechanism than was applied previously.

Possible breaking changes (though unlikely)

New features

Bug fixes

Big change

rxode2et (no changes before merge)

rxode2et 2.0.13

rxode2et 2.0.12

rxode2et 2.0.11

rxode2et 2.0.10

rxode2et 2.0.9

rxode2random (before merge)

rxode2random 2.1.0

rxode2random 2.0.13

rxode2random 2.0.12

rxode2random 2.0.11

rxode2random 2.0.10

rxode2random 2.0.9

rxode2parse (fixed before merging)

rxode2parse 2.0.19

rxode2parse 2.0.18

rxode2parse 2.0.17

rxode2parse 2.0.16

rxode2parse 2.0.15

rxode2parse 2.0.14

rxode2parse 2.0.13

rxode2parse 2.0.12

rxode2parse 2.0.11

rxode2parse 2.0.10

rxode2parse 2.0.9

rxode2 2.1.3

Bug fixes

New features

Other changes

rxode2 2.1.2

Other changes

rxode2 2.1.0

Breaking changes

New features

If you only want to summarize a subset of endpoints, you can focus on the endpoint by pre-pending the endpoint with sim. For example if you wanted to plot/summarize only the endpoint eff you would use sim.eff. (ie confint(model, "sim.eff") or plot(model, sim.eff))

cl <- exp(tcl + eta.cl + wt_cl * log(WT/70.5))

Instead of the

cl <- exp(tcl + eta.cl + wt_cl * log.WT.div.70.5)

That was previously required (where log.WT.div.70.5 was calculated in the data) for mu expressions. The ui now has more information to allow transformation of data internally and transformation to the old mu-referencing style to run the optimization.

Internal new features

Bug fixes

Maintenance fixes

rxode2 2.0.14

rxode2 2.0.13

Bug fixes

New features

Internal changes

rxode2 2.0.11

rxode2 2.0.10

rxode2 2.0.9

rxode2 2.0.8

Breaking changes

New features

Bug fixes

New features

rxode2 2.0.7

rxode2 2.0.6

Breaking changes

Solving controls

Simulations

Other breaking changes

Additional features

Internal changes

Bug fixes

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



nlmixr2/rxode2 documentation built on Jan. 11, 2025, 8:48 a.m.