Provides alternatives to the normal adjusted R-squared estimator for the estimation of the multiple squared correlation in regression models, 
              as fitted by the lm() function. The alternative estimators are described in Karch (2016) <DOI:10.31234/osf.io/v8dz5>.

Evaluates land suitability for different crops production. 
  The package is based on the Food and Agriculture Organization (FAO) and the 
  International Rice Research Institute (IRRI) methodology for land evaluation. 
  Development of ALUES is inspired by similar tool for land evaluation, Land Use 
  Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate 
  land suitability of a particular area based on inputs such as rainfall, temperature, 
  topography, and soil properties. The membership functions used for fuzzy modeling are 
  the following: Triangular, Trapezoidal and Gaussian. The methods for computing the 
  overall suitability of a particular area are also included, and these are the Minimum, 
  Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms 
  written in C++.

Combination of results for meta-analysis using significance
    and effect size only. P-values and fold-change are combined to obtain
    a global significance on each metabolite. Produces a volcano plot
    summarising the relevant results from meta-analysis. Vote-counting
    reports for metabolites. And explore plot to detect discrepancies
    between studies at a first glance. Methodology is described in the
    Llambrich et al. (2021) <doi:10.1093/bioinformatics/btab591>.

Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal 
    Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides
    a novel algorithm for solving the sparse principal component analysis problem which provides
    advantages over existing methods in terms of efficiency and convergence guarantees.
    Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>.
    Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>.
    Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.