Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.

Unbiased estimators of overall and per-class thematic map accuracy and area published in Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015> and Stehman (2014) <doi:10.1080/01431161.2014.930207>.

Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in 'mrgsolve', typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format. The performance of the package was described by Le Louedec et al (2021) <doi:10.1002/psp4.12689>.

Includes support for 'Mapbox' Navigation APIs, including directions, 
  isochrones, and route optimization; the Search API for forward and reverse geocoding; 
  the Maps API for interacting with 'Mapbox' vector tilesets and visualizing 
  'Mapbox' maps in R; and 'Mapbox Tiling Service' and 'tippecanoe' for generating map tiles.
  See <https://docs.mapbox.com/api/> for more information about the 'Mapbox' APIs.

Makes 'Mapbox GL JS' <https://docs.mapbox.com/mapbox-gl-js/api/>,
  an open source JavaScript library that uses WebGL to render interactive maps,
  available within R via the 'htmlwidgets' package. Visualizations can be used from the R console,
  in R Markdown documents and in Shiny apps.

A variety of functions that make it easy to plot standard choropleth maps as well as choropleth alternatives in 'ggplot2'.

Geospatial shapefile data of China administrative divisions to the county/district-level.

Bayesian multivariate age-period-cohort (MAPC) models for analyzing health data, with support for model fitting, visualization, stratification, and model comparison. Inference focuses on identifiable cross-strata differences, as described by Riebler and Held (2010) <doi:10.1093/biostatistics/kxp037>. Methods for handling complex survey data via the 'survey' package are included, as described in Mercer et al. (2014) <doi:10.1016/j.spasta.2013.12.001>.