Enables us to use the functions of the package 'magick' interactively.

Set of utility functions to interact with 'WeMo Switch', a smart 
    plug that can be remotely controlled via wifi. The provided functions make 
    it possible to turn one or more 'WeMo Switch' plugs on and off in a 
    scriptable fashion. More information about 'WeMo Switch' can be found at 
    <http://www.belkin.com/us/p/P-F7C027/>.

Balancing quasi-experimental field research for effects of covariates is fundamental for drawing causal inference. Propensity Score Matching deals with this issue but current
    techniques are restricted to binary treatment variables. Moreover, they provide several solutions without providing a comprehensive framework on choosing the best model. The
    MAGMA R-package addresses these restrictions by offering nearest neighbor matching for two to four groups. It also includes the option to match data of a 2x2 design. In addition, 
    MAGMA includes a framework for evaluating the post-matching balance. The package includes functions for the matching process and matching reporting. We provide a tutorial on 
    MAGMA as vignette. More information on MAGMA can be found in Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022) <https://osf.io/p47nc/>.

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.