Ordered Homogeneity Pursuit Lasso for Group Variable Selection (OHPL)
Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method takes the homogeneity structure in high-dimensional data into account and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.

Unconstrained Numerical Optimization Algorithms (mize)
Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems.

Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors (bayeslm)
Efficient sampling for Gaussian linear regression with arbitrary priors.

Plot Functions for JIF (Journal Impact Factor) and Paper Percentiles (BibPlots)
Currently, the package provides two functions for plotting and analyzing bibliometric data (JIF and paper percentile values). Further extension to more plot variants is planned.

The Bimodality Index (BimodalIndex)
Defines the functions used to compute the bimodal index as defined by Wang et al. (2009) <https://…/>.