The R kernel for the 'Jupyter' environment executes R code which the front-end
    ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.

Fast and memory efficient methods for truncated singular value
    decomposition and principal components analysis of large sparse and dense
    matrices.

The Row-column designs are widely recommended for experimental situations when there are two well-identified factors that are cross-classified representing known sources of variability. These designs are expected to result a gain in accuracy of estimating treatment comparisons in an experiment as they eliminate the effects of the row and column factors. However, these designs are not readily available when the number of treatments is more than the levels of row and column blocking factors. This package named 'iRoCoDe' generates row-column designs with incomplete rows and columns, by amalgamating two incomplete block designs (D1 and D2). The selection of D1 and D2 (the input designs) can be done from the available incomplete block designs, viz., balanced incomplete block designs/ partially balanced incomplete block designs/ t-designs. (Mcsorley, J.P., Phillips, N.C., Wallis, W.D. and Yucas, J.L. (2005).<doi:10.1007/s10623-003-6149-9>).

Imbalanced domain learning has almost exclusively focused on solving 
    classification tasks, where the objective is to predict cases labelled with a 
    rare class accurately. Such a well-defined approach for regression tasks lacked 
    due to two main factors. First, standard regression tasks assume that each value 
    is equally important to the user. Second, standard evaluation metrics focus on 
    assessing the performance of the model on the most common cases. This package 
    contains methods to tackle imbalanced domain learning problems in regression 
    tasks, where the objective is to predict extreme (rare) values.
    The methods contained in this package are: 1) an automatic and non-parametric 
    method to obtain such relevance functions; 2) visualisation tools; 3) suite of 
    evaluation measures for optimisation/validation processes; 4) the squared-error 
    relevance area measure, an evaluation metric tailored for imbalanced regression tasks.
    More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.