Reads, writes, and edits EXIF and other file metadata using
    ExifTool <https://exiftool.org/>, returning read results as a data
    frame. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP
    and ID3, Lyrics3, as well as the maker notes of many digital cameras by
    Canon, Casio, DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf,
    Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson,
    Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo,
    Sigma/Foveon and Sony.

An interactive 'shiny' application that estimates all-cause
    mortality and excess mortality from country-level weekly or monthly death
    counts. Users supply observed deaths and an event calendar (for example
    COVID-19 waves or typhoons); the app fits one or more statistical
    baseline models (historical average, negative binomial regression,
    quasi-Poisson regression, zero-inflated Poisson regression, ARIMA
    (autoregressive integrated moving average) and SARIMA (seasonal ARIMA)
    models, GAM (generalized additive model) splines, and the model of
    Karlinsky and Kobak (2021) <doi:10.7554/eLife.69336>) on a user-defined
    baseline period, projects the expected deaths into the post-baseline
    period, and reports excess deaths, P-scores (excess deaths as a
    percentage of expected deaths) and confidence limits with tables, plots
    and downloadable reports. Launch the application with run_app().

The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) <doi:10.48550/arXiv.2205.15111>.