Generalized Tensor Regression with Covariates on Multiple Modes (tensorregress)
Implement the generalized tensor regression in Xu, Hu and Wang (2019) <arXiv:1910.09499>. Solve tensor-response regression given covariates on multiple modes with alternating updating algorithm.

R Wrappers for EXPOKIT; Other Matrix Functions (rexpokit)
Wraps some of the matrix exponentiation utilities from EXPOKIT (<http://…/> ), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. ‘Expokit: A Software Package for Computing Matrix Exponentials.’ ACM Trans. Math. Softw. 24(1): 130-156). EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0). Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well.

Automatic Calculation of Effects for Piecewise Structural Equation Models (semEff)
Provides functionality to automatically calculate direct, indirect, and total effects from piecewise structural equation models, comprising lists of fitted models representing structured equations (Lefcheck 2016 <doi:10/f8s8rb>). Confidence intervals are provided via bootstrapping.

Cluster Circular Systematic Sampling (ccss)
Draws systematic samples from a population that follows linear trend. The function returns a matrix comprising of the required samples as its column vectors. The samples produced are highly efficient and the inter sampling variance is minimum. The scheme will be useful in various field like Bioinformatics where the samples are expensive and must be precise in reflecting the population by possessing least sampling variance.