Homogenize GNSS (Global Navigation Satellite System) time-series. The general model is a segmentation in the mean model including a periodic function and considering monthly variances,  see Quarello (2020) <arXiv:2005.04683>.

Read data files readable by 'gnumeric' into 'R'. Can read
        whole sheet or a range, from several file formats, including
        the native format of 'gnumeric'. Reading is done by using
        'ssconvert' (a file converter utility included in the 'gnumeric'
        distribution <http://www.gnumeric.org>) to convert
        the requested part to CSV. From 'gnumeric' files (but not other
        formats) can list sheet names and sheet sizes or read all
        sheets.

This function converts mfpr, numeric, or character strings representing numbers to bigq format without loss of precision.

Solves goal programming problems of the weighted and  
    lexicographic type, as well as combinations of the two, as described 
    by Ignizio (1983) <doi:10.1016/0305-0548(83)90003-5>. Allows for 
    a simple human-readable input describing the problem as a series 
    of equations. Relies on the 'lpSolve' package to solve the underlying 
    linear optimisation problem.

Perform gene set enrichment analyses using the Gene set Ordinal 
    Association Test (GOAT) algorithm and  visualize your 
    results. Koopmans, F. (2024) <doi:10.1101/2023.12.10.570979>.

Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.

A collection of functions useful in (vegetation) community analyses and ordinations. Includes automatic species selection for ordination diagrams, NMDS stress/scree plots, species response curves, merging of taxa as well as calculation and sorting of synoptic tables.

Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details.