Nowcasting right-truncated epidemiological data is critical
  for timely public health decision-making, as reporting delays can create
  misleading impressions of declining trends in recent data. This package
  provides nowcasting methods based on using empirical
  delay distributions and uncertainty from past performance. It is also designed
  to be used as a baseline method for developers of new nowcasting methods.
  For more details on the performance of the method(s) in this package applied
  to case studies of COVID-19 and norovirus, see our recent paper at
  <https://wellcomeopenresearch.org/articles/10-614>. The package supports
  standard data frame inputs with reference date, report date, and
  count columns, as well as the direct use of reporting triangles, and is
  compatible with 'epinowcast' objects. Alongside
  an opinionated default workflow, it has a low-level pipe-friendly modular
  interface, allowing context-specific workflows. It can accommodate a wide
  spectrum of reporting schedules, including mixed patterns of reference and
  reporting (daily-weekly, weekly-daily). It also supports sharing delay
  distributions and uncertainty estimates between strata,
  as well as custom uncertainty models and delay estimation methods.

A lightweight package to access spatial basemaps from open sources such as 'OpenStreetMap', 'Carto', 'Mapbox' and others in R.

Providing equivalent functions for the dummy
    classifier and regressor used in 'Python' 'scikit-learn' library. Our goal
    is to allow R users to easily identify baseline performance for their
    classification and regression problems. Our baseline models use no
    predictors, and are useful in cases of class imbalance, multiclass
    classification, and when users want to quickly identify how much
    improvement their statistical and machine learning models are over several
    baseline models. We use a "better" default (proportional guessing) for
    the dummy classifier than the 'Python' implementation ("prior", which is
    the most frequent class in the training set). The functions in the
    package can be used on their own, or introduce methods named
    'dummy_regressor' or 'dummy_classifier' that can be used within the
    caret package pipeline.