Performs fast variable selection in high-dimensional settings while controlling the 
    false discovery rate (FDR) at a user-defined target level. The package is based on the paper 
    Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>.

Tri-hierarchical incomplete block design is defined as an arrangement of v  treatments each replicated r  times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) <doi:10.1093/biomet/54.3-4.479>. This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions.

Assists in analyzing the lying behavior of cows from raw data 
    recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows 
    the determination of common measures for lying behavior including total lying duration, the 
    number of lying bouts, and the mean duration of lying bouts. Further capabilities are the 
    description of lying laterality and the calculation of proxies for the level of physical 
    activity of the cow. Reference: Simmler M., Brouwers S. P. (2024) <doi:10.7717/peerj.17036>.