An interface to the 'AutoDesk' 'API' Platform including the Authentication 
    'API' for obtaining authentication to the 'AutoDesk' Forge Platform, Data Management 
    'API' for managing data across the platform's cloud services, Design Automation 'API'
    for performing automated tasks on design files in the cloud, Model
    Derivative 'API' for translating design files into different formats, sending
    them to the viewer app, and extracting design data, and Viewer for rendering 
    2D and 3D models.

A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.

Automatic fixed rank kriging for (irregularly located)
    spatial data using a class of basis functions with multi-resolution features
    and ordered in terms of their resolutions. The model parameters are estimated
    by maximum likelihood (ML) and the number of basis functions is determined
    by Akaike's information criterion (AIC). For spatial data with either one
    realization or independent replicates, the ML estimates and AIC are efficiently
    computed using their closed-form expressions when no missing value occurs. Details 
    regarding the basis function construction, parameter estimation, and AIC calculation  
    can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For
    data with missing values, the ML estimates are obtained using the expectation-
    maximization algorithm. Apart from the number of basis functions, there are
    no other tuning parameters, making the method fully automatic. Users can also
    include a stationary structure in the spatial covariance, which utilizes
    'LatticeKrig' package.