Provides an interface to access and analyze data from
    'DeFiLlama'<https://defillama.com>. This package simplifies the
    process of fetching and manipulating 'DeFiLlama' data for further
    analysis and visualization.

Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.

Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.

R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.

A very lightweight package that writes out log messages in an opinionated way.
  Simpler and lighter than other logging packages, 'rlog' provides a compact feature set that
  focuses on getting the job done in a Unix-like way.

Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.

Rapid, simulation-based exact (restricted) likelihood ratio tests
    for testing the presence of variance components/nonparametric terms for
    models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(),
    mgcv::gamm() and SemiPar::spm().

Random forest with a variety of additional features for regression, classification and survival analysis. 
             The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable,
             based on Zhu, Zeng & Kosorok (2015) <doi:10.1080/01621459.2015.1036994>, subject weight, variable weight, 
             tracking subjects used in each tree, etc.