Package __optimr__ is now on CRAN, and a more capable package __optimrx__ is on R-forge at https://r-forge.r-project.org/R/?group_id=395 These packages wrap a number of tools for function minimization, sometimes with bounds constraints or fixed parameters, but use a consistent interface in function optimr() that matches the base function optim(). Moreover, the use of the parameter scaling control parscale is applied to all methods. There are functions to allow multiple methods to be tried simultaneously via function opm(), which is a replacement for package optimx that used a different calling syntax. There are multistart() and polyopt() functions for multiple starts or polyalgorithm uses. Some of the approximately twenty available optimizers require derivative (gradient) information, and the calling syntax uses gradient routine names in quotation marks to specify which gradient approximations are to be used. Nevertheless, I strongly recommend analytic gradients where possible, and welcome any efforts to find user-friendly ways to provide automatic or symbolic differentiation. As the main optimr() function is set up to permit new optimizers to be added, there is a vignette explaining (or trying to!) how to add another optimizer. Package optimr deliberately uses just a few optimizers to avoid reverse dependency issues should some optimizers be dropped from CRAN. Otherwise the usage should be the same. I welcome communications from those who add optimizers or features. Indeed, as I have now been retired from teaching for 8 years, it would not be amiss for the maintainer job to be shared with someone younger. For communications about extensions of the packages or help with maintenance of this and related tools, please get in touch off-line to either the email above or nashjc _at_ uottawa.ca. Because of the dependency on many other packages, it is likely there will be glitches from time to time as underlying software is adjusted, maintained or improved. Often sorting out exactly where the difficulties arise can be tricky, and I would repeat the R mantra of "short reproducible example". As always with complicated packages like this, there will certainly be some ways to get unexpected responses, and I ask your indulgence and assistance in rendering the packages water-tight. Cheers, John Nash