A growing number of neuroimaging tools are being developed to enable data analysis in a more tool-independent manner when analyzing three- and four-dimensional brain imaging data in various statistical settings. Ideally, such tools still offer user interfaces that are accessible to those with a basic understanding of programming and statistics. Examples for traditional methods for doing statistical computations in voxel-space for brain diffusion tensor imaging (DTI) data in brain image processing are Tract-Based Spatial Statistics (TBSS) and Statistical Parametric Mapping (SPM). Although these tools contain methods for fundamental statistical analysis, they lack the scalability necessary for application to larger datasets and integration of advanced statistical methods such as machine learning. Furthermore, the lack of support for more complex statistical operations make it more difficult to identify more complex patterns that voxel-level analysis would reveal.skiftiTools offers a flexible package that seamlessly leverages the expansive set of statistical operations in R and other tools, by writing 3-dimensional voxelwise data into tab-separated values ASCII files, facilitating interoperability with, for example, the R language (RStudio), SPSS, and SAS. Following statistical processing, the data that is produced can be read in to skiftiTools again for visualization. The software supports the tab-separated ASCII format, the Nifti image format, and its own stand-alone format. Built with the R programming language, it is freely available at https://github.com/haanme/skiftiTools and is simple to install from R's CRAN package repository. Basic features available also in docker containers. See https://skiftitools.readthedocs.io for documentation.