Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal 
    Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides
    a novel algorithm for solving the sparse principal component analysis problem which provides
    advantages over existing methods in terms of efficiency and convergence guarantees.
    Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>.
    Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>.
    Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.

Tools for Clustering and Principal Component Analysis
        (With robust methods, and parallelized functions).

Getting and parsing data of location geocode/reverse-geocode and administrative regions from 'AutoNavi Maps'<https://lbs.amap.com/api/webservice/summary> API.

Build and control interactive 2D and 3D maps with 'R/Shiny'.  Lean set of powerful commands wrapping native calls to 'AMap' <https://lbs.amap.com/api/jsapi-v2/summary/>.
    Deliver rich mapping functionality with minimal overhead.

Read, manipulate and write voxel spaces. Voxel spaces are
    read from text-based output files of the 'AMAPVox' software. 'AMAPVox'
    is a LiDAR point cloud voxelisation software that aims at estimating
    leaf area through several theoretical/numerical approaches. See more
    in the article Vincent et al. (2017) <doi:10.23708/1AJNMP> and the
    technical note Vincent et al. (2021) <doi:10.23708/1AJNMP>.

Collect  your data on digital marketing campaigns from Amazon Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Collect  your data on digital marketing campaigns from Amazon S3 using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Collect  your data on digital marketing campaigns from Amazon Sp using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Generation of natural looking noise has many application
    within simulation, procedural generation, and art, to name a few. The
    'ambient' package provides an interface to the 'FastNoise' C++ library
    and allows for efficient generation of perlin, simplex, worley, cubic,
    value, and white noise with optional perturbation in either 2, 3, or 4
    (in case of simplex and white noise) dimensions.