Provides tools to download, process, and analyze data from the
    International Monetary Fund's World Economic Outlook (WEO) database 
    <https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases>. 
    Functions support downloading complete WEO releases, accessing specific 
    economic indicators for selected countries, and listing available data.

Provides ability to create color palettes from image files. It 
    offers control over the type of color palette to derive from an image 
    (qualitative, sequential or divergent) and other palette properties.
    Quantiles of an image color distribution can be trimmed. Near-black or 
    near-white colors can be trimmed in RGB color space independent of trimming 
    brightness or saturation distributions in HSV color space. Creating 
    sequential palettes also offers control over the order of HSV color 
    dimensions to sort by. This package differs from other related packages 
    like 'RImagePalette' in approaches to quantizing and extracting colors in 
    images to assemble color palettes and the level of user control over 
    palettes construction.

Provides an interface for image recognition using the 'Google Vision API' <https://cloud.google.com/vision/> .  Converts API data for features such as object detection and optical character recognition to data frames. The package also includes functions for analyzing image annotations.

Estimating height of forest plant is one of the key challenges of recent times. This package will help to fit and validate AI (Artificial Intelligence) based machine learning algorithms for estimation of height of conifer trees based on diameter at breast height as explanatory variable using algorithm of Paul et al. (2022) <doi:10.1371/journal.pone.0270553>..

Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.