Data frame class for storing collective movement data (e.g. fish
    schools, ungulate herds, baboon troops) collected from GPS trackers or 
    computer vision tracking software.

Collaborative writing and editing of R Markdown (or Sweave) documents. The local .Rmd (or .Rnw) is uploaded as a plain-text file to Google Drive. By taking advantage of the easily readable Markdown (or LaTeX) syntax and the well-known online interface offered by Google Docs, collaborators can easily contribute to the writing and editing process. After integrating all authors’ contributions, the final document can be downloaded and rendered locally.

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 values, gradients, and Hessians 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.

Pest monitoring is crucial, especially during the early season, to understand the distribution and the proliferation of the target pest. Raw count data from pest monitoring/traps can be coupled with environmental variables such as temperature, growing degree-day ('GDD') etc. to get useful insights about the pest phenology. This package pulls temperature data from the California Irrigation Management Information System ('CIMIS', <https://cimis.water.ca.gov>), the 'Daymet' application programming interface ('API', <https://daymet.ornl.gov>), or 'Open Meteo' ('API', <https://open-meteo.com/>) sequentially for a user-specified time period and calculates cumulative growing degree-days. Users provide pest development thresholds (lower and upper temperatures), pest of concern, and the geographic coordinates of the trap location to track emergence and phenology.