Making a package from base R files

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  • John C. Nash, retired professor, Telfer School of Management, University of Ottawa
  • Arkajyoti Bhattacharjee, Department of Mathematics and Statistics, Indian Institute of Technology, Kanpur
  • based on communications from Duncan Murdoch

11/06/2021, revised 19/01/2022


This article tries to explain an approach to developing alternative versions of functions which are in the distributed base of R. Our interest was in developing improvements to the nls() function and related features in R as part of a Google Summer of Code project for which Arkajyoti Bhattacharjee was the funded student. However, nls() has many tentacles involving a number of files and functions that may or may not be called as nls() is executed.

Part of the difficulty in carrying out such development of alternative versions is that one needs to be able to execute the new variants in parallel with the existing ones. A heavy-effort approach would be to have separate full sets of R code and build each system and run them separately. That is, we want to have two or more versions of R in the same computing system.

There are several ways to do this:

  • build or install the two versions of R in separate directory trees and invoke them with separate launch scripts or programs, such as described in \url{};
  • use virtual machines with different R versions;
  • run the different R versions in containers, e.g., Carl Boettiger, by, and Dirk Eddelbuettel (2017);
  • move the required functionality to a package that can be loaded as needed to mask the base functionality and replace it with the functions we wish to test or use. This last approach is discussed here.

We did attempt to provide R functions to switch between alternative sets of working functions — e.g., ReplaceNLS.R and RestoreNLS.R — but this approach was not promising, and not pursued.

Duncan Murdoch suggested, and generously provided an almost-complete prototype of, a package of interlocked functions that provide the set of nls() capabilities. This package nlspkg was copied to nlsalt then modified to provide the new capabilities or new expression of the code. The new capabilities or code, such as a function Anyfn() can be tested side by side with a “standard” version (if one exists) by use of the double colon syntax, namely,

nlspkg::Anyfn() # existing functionality or code
# and
nlsalt::Anyfn() # New functionality of code.

Packaging from base R files

Duncan Murdoch described his process of developing the prototype of

Identify the main R functions I wanted in the package, and copy that
code into ./R.

Identify the C/Fortran functions that are referenced, and copy the
source for that into ./src.

Try to build, and get lots of errors about missing functions. Find
those and copy as above.

There was one case (the `%||%` operator) where I thought the code used an unexported object from utils; the base-internals.R function just used ::: to get it, but a better solution would be to copy the source (which is pretty trivial), and it turns out, is what stats did (the duplicate source is in AIC.R).

Some files contained functions or methods that weren't relevant to us (e.g. confint.lm).  I commented those out so I didn't need to worry about the functions they used, and imported the generic (i.e. confint) from stats.

In the C code, we were lucky because the nls code only made use of one C function that's not in the API:  R_NewEnv. (That was called from numeric_deriv.) There's an R function to do that, so I called it and passed the result in.

Finally, once it would build, I tried running R CMD check.  This
identified all the missing help pages, which I copied over.  They didn't need any modifications.

Because nls isn't really base functionality, all of this was a lot
easier than if I had tried to move something that works more with the internals.  That's probably true for most functions in stats, but would not be true for other base packages (except datasets).

After we downloaded the ZIP file of Duncan’s package from his GitHub repository,
we built and checked the package. There were two WARNING messages:

* checking DESCRIPTION meta-information ... WARNING
Non-standard license specification:
  GPL-2, GPL-3
Standardizable: FALSE
* checking for missing documentation entries ... WARNING
Undocumented code objects:
  ‘C_bvalus’ ‘C_lowesp’ ‘C_lowesw’ ‘C_nls_iter’ ‘C_numeric_deriv’
  ‘C_port_ivset’ ‘C_port_nlminb’ ‘C_port_nlsb’ ‘C_pppred’ ‘C_rbart’
  ‘C_setppr’ ‘C_setsmu’ ‘C_smart’ ‘C_supsmu’ ‘anova.nls’
  ‘anovalist.nls’ ‘coef.nls’ ‘confint.nls’ ‘deviance.nls’
  ‘df.residual.nls’ ‘fitted.nls’ ‘formula.nls’ ‘logLik.nls’ ‘nlsModel’
  ‘nlsModel.plinear’ ‘nls_port_fit’ ‘nobs.nls’ ‘plot.profile.nls’
  ‘port_cpos’ ‘port_get_named_v’ ‘port_msg’ ‘port_v_nms’ ‘predict.nls’
  ‘print.nls’ ‘print.summary.nls’ ‘profiler’ ‘profiler.nls’
  ‘residuals.nls’ ‘summary.nls’ ‘vcov.nls’ ‘weights.nls’
All user-level objects in a package should have documentation entries.
See chapter ‘Writing R documentation files’ in the ‘Writing R
Extensions’ manual.

The first WARNING can be overcome by changing the License line in the package

License: GPL-2, GPL-3


License: GPL-2 | GPL-3

as noted in \url{}.

The warnings about documentation come from two sources:

  • many of the nls() features parallel generic functions such as residuals,
    predict, coef, and so on, so are generically documented in R.
  • the rest of the “undocumented” functions are C functions (identifiable easily
    as their names contain underscore characters). It is these functions
    that our “Improvements to nls()” project aims to replace where it is sensible to
    do so with R functions. They may then be documented as needed.

Name issues — IMPORTANT

It is worth mentioning that the NAMESPACE file provided by Duncan is an important part of the packaging. It could be worthwhile to document each element in that file needed or a package such as nlspkg, but at the time of writing, this is yet to be carried out. however, we must note that in renaming the package from nlspkg to something else, the change of name must be mirrored in the line

useDynLib(nlspkg, .registration = TRUE, .fixes = "C_")

where nlspkg in this line must be changed appropriately.

We also noted that the S3method() entries of the NAMESPACE files are needed to link the generic functions to similar modeling functions in R. Thus anova, coef, etc. are declared as S3 methods.

As a convenience to get the package running, Duncan used the shortcut


to ensure functions were made available to the user. This makes visible all the internal C functions, named C_(something). We have suppressed this blanket declaration and explicitly exported the nls and numericDeriv functions. One or two other routines may need to be exported to allow full functioning of the package.

We note that name change is ALSO required in src/init.c in the line

void attribute_visible R_init_nlspkg(DllInfo *dll)

For version control, the file .gitignore has two entries: one is *.0 and
the other, initially, was We changed the latter to *.so and this
appears to be satisfactory for general use.

Developing an alternative set of functions and an alternative package

We wished to build our alternative package by replacing calls to C functions with R work-alike functions. A preliminary example that already is set up and has passed preliminary checks is the function numericDeriv() from the file R/nls.R with called elements from the file src/nls.c. Note that some code needs to be commented out or deleted from the C file(s) and headers to avoid errors when building and checking the alternative package.

To prepare the nlsalt package a directory of that name was created and the contents of the nlspkg directory copied over. The DESCRIPTION file was edited to provide the nlsalt name and the result checked with build and CHECK.

At this stage, we can begin to substitute for calls to C code and remove that code once replacement R material is present. Frequent build and CHECK cycles are helpful.

For RStudio users, note that each package directory needs its own (name).Rproj file, created using RStudio’s “File/New Project” etc. dialog. However, the package directories with their Rproj files can be within the umbrella version control project improvenls for the Git version control. (We used a GitLab repository.)

Windows issue

While all the above worked well in a Linux environment, when we tried running the build and CHECK in a Windows environment, we found errors. (JN normally runs in Linux Mint 20.x, but AB uses Windows 10. Each of us has a VirtualBox VM for the alternative OS to allow for checking of our work.) There is a load error for functions from the BLAS (Basic Linear Algebra Subroutines) collection in a 32 bit Windows architecture. This is revealed by the RStudio “Build / Clean and Rebuild” feature. Using the CMD.exe terminal window showed that R CMD build worked, but that R CMD INSTALL gave the load errors.

After some exchange of emails with others and with the R-package-devel mailing list, a Google search using terms suggested in the emails found the posting


This suggested that a file is needed in the src/ directory of the package(s), and that this file should contain just the following line.


We did, however, note that it is not clearly indicated that this file needs to be in
src/, nor (until revealed by R CMD check) that the file must be and
not There is a lot of material on Makevars in the Writing R
Extensions manual (R Core Team, 2013), but these points do not seem to be presented, or at least are not obvious.

Our packaged version of nls() from July 2021 is at


Note that this is drawn from the version of R of that date (R 4.1.0), so will not necessarily reflect the distributed version of R when this document is read.

Downstream benefit

The approach to replacing functionality in base R suggested here has a follow-on benefit should some or all of the improvements we hope to be able to make are eventually included in R’s distribution files. That benefit is that the initial package can be renamed to, for example, nlslegacy so that R users can, should they need, use legacy capabilities. Indeed, from preliminary timings of some functions, it appears that pure R code runs 2-3 times slower than the mixed R and C versions. On the other hand, the pure R code in our early cases is much shorter and, we suspect, much easier to maintain. Furthermore, it simplifies the packaging of base R for alternative interpreter or compiler versions of the R language.


It is a pleasure to note the help and encouragement of Duncan Murdoch, Dirk Eddelbuettel and Brett Klamer.


Carl Boettiger, by, and Dirk Eddelbuettel. 2017. “An Introduction to Rocker: Docker Containers for R.” Journal of Statistical Software 9 (2).

R Core Team. 2013. “Writing R Extensions.” exts.html.

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