A collection of different indices and visualization techniques for evaluate the seed germination process in ecophysiological studies (Lozano-Isla et al. 2019) <doi:10.1111/1440-1703.1275>.

Provides functions to compute various germination indices
    such as germinability, median germination time, mean germination time,
    mean germination rate, speed of germination, Timson's index,
    germination value, coefficient of uniformity of germination,
    uncertainty of germination process, synchrony of germination etc. from
    germination count data. Includes functions for fitting cumulative seed
    germination curves using four-parameter hill function and computation
    of associated parameters. See the vignette for more, including full
    list of citations for the methods implemented.

Simple git client for R based on 'libgit2' <https://libgit2.org> with
    support for SSH and HTTPS remotes. All functions in 'gert' use basic R data 
    types (such as vectors and data-frames) for their arguments and return values.
    User credentials are shared with command line 'git' through the git-credential
    store and ssh keys stored on disk or ssh-agent.

Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.

Reproducible, programmatic retrieval of datasets from the
    GESIS Data Archive.  The GESIS Data Archive <https://search.gesis.org>  
    makes available thousands of invaluable datasets, but researchers using
    these datasets are caught in a bind.  The archive's terms and conditions
    bar dissemination of downloaded datasets to third parties, but to ensure 
    that one's work can be reproduced, assessed, and built upon by others, one
    must provide access to the raw data one has employed.  The 'gesisdata'
    package cuts this knot by providing registered users with programmatic,
    reproducible access to GESIS datasets from within 'R'.