Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.

Provides an integrated user interface and workflow for
             the analysis of running, cycling and swimming data from GPS-enabled
             tracking devices through the 'trackeR' <https://CRAN.R-project.org/package=trackeR> R package.

Tracks parameter value, gradient, and Hessian at each iteration 
    of numerical optimizers. Useful for analyzing optimization progress, 
    diagnosing issues, and studying convergence behavior.

Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.

Functions for working with magnetic resonance images. Reading and
    writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH);
    interactive and non-interactive visualisation; flexible image manipulation;
    metadata and sparse image handling.

A collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) <doi:10.1016/S0047-2727(00)00085-2> and Froeb et al. (2003) <doi:10.1016/S0304-4076(02)00166-5>.

Datasets from Yotov, et al. (2016, ISBN:978-92-870-4367-2) "An
    Advanced Guide to Trade Policy Analysis" and functions to report regression
    summaries with clustered robust standard errors.