Total Variation Regularization (tvR)
Provides tools for denoising noisy signal and images via Total Variation Regularization. Reducing the total variation of the given signal is known to remove spurious detail while preserving essential structural details. For the seminal work on the topic, see Rudin et al (1992) <doi:10.1016/0167-2789(92)90242-F>.

Time Series Forecasting with Neural Networks (nnfor)
Contains functions to facilitate automatic time series modelling with neural networks. Allows fully automatic, semi-manual or fully manual specification of networks. For details of the specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>; and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.

Factor Adjusted Robust Multiple Testing (FarmTest)
Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a robust procedure to estimate distribution parameters using the Huber’s loss function and accounts for strong dependence among coordinates via an approximate factor model. This method is particularly suitable for high dimensional data when there are many variables but only a small number of observations available. Moreover, the method is tailored to cases when the underlying distribution deviates from Gaussian, which is commonly assumed in the literature. Besides the results of hypotheses testing, the estimated underlying factors and diagnostic plots are also output. Multiple comparison correction is done after estimating the proportion of true null hypotheses using the method in Storey (2015) <https://…/qvalue>. See the paper on the ‘FarmTest’ method, Zhou et al.(2017) <https://goo.gl/68SJpd>, for detailed description of methods and further references.

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