The 'tmap' package provides two plotting modes for static and interactive thematic maps. This package extends 'tmap' with two additional modes based on 'Mapbox GL JS' and 'MapLibre GL JS'. These modes feature interactive vector tiles, globe views, and other modern web-mapping capabilities, while maintaining a consistent 'tmap' interface across all plotting modes.

Provides functions for visualizing networks with 'tmap'. It supports 'sfnetworks' objects natively but is not limited to them. Useful for adding network layers such as edges and nodes to 'tmap' maps. More features may be added in future versions.

Provides support for a variety of spatial data sources in 'tmap', 
    including remote, tiled, and streaming formats. Enables the use 
    of external vector and raster data without requiring full data 
    import, facilitating efficient visualization workflows.

Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps.

Attaches a set of packages commonly used for spatial plotting with 'tmap'. It includes 'tmap' and its extensions ('tmap.glyphs', 'tmap.networks', 'tmap.cartogram', 'tmap.mapgl'), as well as supporting spatial data packages ('sf', 'stars', 'terra') and 'cols4all' for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem.

With this tool, a user should be able to quickly implement
    complex random effect models through simple C++ templates. The package combines
    'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector
    library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain
    an efficient implementation of the applied Laplace approximation with exact
    derivatives. Key features are: Automatic sparseness detection, parallelism
    through 'BLAS' and parallel user templates.

Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) <DOI:10.1371/journal.pone.0197954>.