Generate XMR Control Chart Data from Time-Series Data (xmrr)
XMRs combine X-Bar control charts and Moving Range control charts. These functions also will recalculate the reference lines when significant change has occured.

Bayesian Variable Selection in High Dimensional Settings using Non-Local Prior (BVSNLP)
Variable/Feature selection in high or ultra-high dimensional settings has gained a lot of attention recently specially in cancer genomic studies. This package provides a Bayesian approach to tackle this problem, where it exploits mixture of point masses at zero and nonlocal priors to improve the performance of variable selection and coefficient estimation. It performs variable selection for binary response and survival time response datasets which are widely used in biostatistic and bioinformatics community. Benefiting from parallel computing ability, it reports necessary outcomes of Bayesian variable selection such as Highest Posterior Probability Model (HPPM), Median Probability Model (MPM) and posterior inclusion probability for each of the covariates in the model. The option to use Bayesian Model Averaging (BMA) is also part of this package that can be exploited for predictive power measurements in real datasets.

Abstract Classes for Building ‘scikit-learn’ Like API (mlapi)
Provides ‘R6’ abstract classes for building machine learning models with ‘scikit-learn’ like API. <http://…/> is a popular module for ‘Python’ programming language which design became de facto a standard in industry for machine learning tasks.

Forecast Combination Methods (ForecastComb)
Provides geometric- and regression-based forecast combination methods under a unified user interface for the packages ‘ForecastCombinations’ and ‘GeomComb’. Additionally, updated tools and convenience functions for data pre-processing are available in order to deal with common problems in forecast combination (missingness, collinearity). For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>, and Clemen RT (1989). <doi:10.1016/0169-2070(89)90012-5>.

Inference and Clustering of Functional Data (gmfd)
Some methods for the inference and clustering of univariate and multivariate functional data, using a generalization of Mahalanobis distance, along with some functions useful for the analysis of functional data. For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.

Tests in Linear Mixed Effects Models (lmerTest)
Different kinds of tests for linear mixed effects models as implemented in ‘lme4’ package are provided. The tests comprise types I – III F tests for fixed effects, LR tests for random effects. The package also provides the calculation of population means for fixed factors with confidence intervals and corresponding plots. Finally the backward elimination of non-significant effects is implemented.