An implementation for the multi-task Gaussian processes with common 
    mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', 
    are available to perform predictions for supervised learning problems, in
    particular for time series or any functional/continuous data applications.
    The corresponding articles has been respectively proposed by Arthur Leroy, 
    Pierre Latouche, Benjamin Guedj and Servane Gey (2022) 
    <doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche, 
    Benjamin Guedj and Servane Gey (2023) <https://jmlr.org/papers/v24/20-1321.html>.
    Theses approaches leverage the learning of cluster-specific mean processes,
    which are common across similar tasks, to provide enhanced prediction
    performances (even far from data) at a linear computational cost (in
    the number of tasks).  'MagmaClust' is a generalisation of 'Magma'
    where the tasks are simultaneously clustered into groups, each being
    associated to a specific mean process.  User-oriented functions in the
    package are decomposed into training, prediction and plotting
    functions. Some basic features (classic kernels, training, prediction) of
    standard Gaussian processes are also implemented.

A client for interacting with 'magma', the data warehouse of the
    'UCSF Data Library'. 'magmaR' includes functions for querying and
    downloading data from 'magma', in order to enable working with such data in
    R, as well as for uploading local data to 'magma'.

Correlates variation within the meta-genome to target species
    phenotype variations in meta-genome with association studies. Follows
    the pipeline described in Chaston, J.M. et al. (2014) <doi:10.1128/mBio.01631-14>.

Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) <DOI:10.2307/2531148>. Implements Fortran 77 code for the methods developed by Schouten (1982) <DOI:10.1111/j.1467-9574.1982.tb00774.x>. Includes estimates of average agreement for each observer and average agreement for each subject.