Adjust Estimates of Learning for Guessing. The package provides 
    standard guessing correction, and a latent class model that leverages
    informative pre-post transitions. For details of the latent class model,
    see <http://gsood.com/research/papers/guess.pdf>.

We consider the ultrahigh-dimensional and error-prone data. Our goal aims to estimate the precision matrix and identify the graphical structure of the random variables with measurement error corrected. We further adopt the estimated precision matrix to the linear discriminant function to do classification for multi-label classes.

A nice GUI for financial DErivatives in R.

Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

'Guild AI' is an open-source tool for managing machine learning
    experiments. It's for scientists, engineers, and researchers who want to
    run scripts, compare results, measure progress, and automate machine
    learning workflow. 'Guild AI' is a light weight, external tool that runs
    locally. It works with any framework, doesn't require any changes to
    your code, or access to any web services. Users can easily record
    experiment metadata, track model changes, manage experiment artifacts,
    tune hyperparameters, and share results. 'Guild AI' combines features
    from 'Git', 'SQLite', and 'Make' to provide a lab notebook for machine
    learning.

A collection of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and  the guilds sampling formula conditioned on guild size (Janzen et al. 2015).