Forest-based statistical estimation and inference.
  GRF provides non-parametric methods for heterogeneous treatment effects estimation
  (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables),
  as well as least-squares regression, quantile regression, and survival regression,
  all with support for missing covariates.

Implements statistical methods for group factor analysis, focusing on estimating the number of global and local factors and extracting them. Several algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008>, Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the Aggregated Projection Method by Hu et al. (2025) <doi:10.1080/01621459.2025.2491154>.

Functions for rendering Bezier
             curves (Pomax, 2018) <https://pomax.github.io/bezierinfo/>
             in 'grid'.  
             There is support for both quadratic and cubic Bezier curves.
             There are also functions for calculating points on curves,
             tangents to curves, and normals to curves.

Estimates grid type bivariate copula functions, calculates some association measures and provides several copula graphics.

Functions for drawing scene trees representing 
              scenes that have been drawn using grid graphics.

Provides a number of user-level functions to work with "grid"
    graphics, notably to arrange multiple grid-based plots on a page, and draw
    tables.

Ridge regression due to Hoerl and Kennard (1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized tuning parameters.
 These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors.
 Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.