Nonlinear regression models are applied in a broad variety of scientific fields. Various R functions are already dedicated to fitting such models, among which the function nls() has a prominent position. Unlike linear regression fitting of nonlinear models relies on non-trivial assumptions and therefore users are required to carefully ensure and validate the entire modeling. Parameter estimation is carried out using some variant of the leastsquares criterion involving an iterative process that ideally leads to the determination of the optimal parameter estimates. Therefore, users need to have a clear understanding of the model and its parameterization in the context of the application and data considered, an a priori idea about plausible values for parameter estimates, knowledge of model diagnostics procedures available for checking crucial assumptions, and, finally, an understanding of the limitations in the validity of the underlying hypotheses of the fitted model and its implication for the precision of parameter estimates. Current nonlinear regression modules lack dedicated diagnostic functionality. So there is a need to provide users with an extended toolbox of functions enabling a careful evaluation of nonlinear regression fits. To this end, we introduce a unified diagnostic framework with the R package nlstools. In this paper, the various features of the package are presented and exemplified using a worked example from pulmonary medicine.

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Advances in DNA sequencing and the increasing number of sequences available in databases have greatly enhanced the bacterial identification process. Several species within the genus Mycobacterium cause serious human and animal diseases. In order to assess their relative positions in the evolutionary process, four gene fragments, from the 16S rRNA (564 bp), hsp65 (420 bp), rpoB (396 bp) and sod (408 bp) genes, were sequenced from 97 strains, including all available type strains of the genus Mycobacterium. The results demonstrate that, in this case, the concatenation of different genes allows significant increases in the power of discrimination and the robustness of the phylogenetic tree. The sequential and/or combined use of sequences of several genes makes it possible to refine the phylogenetic approach and provides a molecular basis for accurate species identification.