Matrix eQTL is designed for fast eQTL analysis on large datasets.
        Matrix eQTL can test for association between genotype 
        and gene expression using linear regression 
        with either additive or ANOVA genotype effects.
        The models can include covariates to account for factors 
        as population stratification, gender, and clinical variables. 
        It also supports models with heteroscedastic and/or correlated errors,
        false discovery rate estimation and 
        separate treatment of local (cis) and distant (trans) eQTLs.
        For more details see Shabalin (2012) <doi:10.1093/bioinformatics/bts163>.

Extends sparse matrix and vector classes from the 'Matrix' package by providing: 
  (a) Methods and operators that work natively on CSR formats (compressed sparse row, 
  a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), 
  mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); 
  (b) Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, 
  including the 'float32' type from 'float'; 
  (c) Coercion methods between pairs of classes which are not present in 'Matrix', 
  such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; 
  (d) Utility functions for sparse matrices such as sorting the indices or removing 
  zero-valued entries; 
  (e) Fast transposes that work by outputting in the opposite storage format;
  (f) Faster replacements for many 'Matrix' methods for all sparse types, such as
  slicing and elementwise multiplication.
  (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.

Constructs the normalized Laplacian matrix of a square matrix, returns the eigenvectors (singular vectors) and visualization of normalized Laplacian map.

Fits the penalized matrix-normal model to be used for linear discriminant analysis with matrix-valued predictors. For a description of the method, see Molstad and Rothman (2018) <doi:10.1080/10618600.2018.1476249>.