The Nordklim dataset 1.0 is a unique and useful achievement for climate 
    analysis. It includes observations of twelve different climate elements from 
    more than 100 stations in the Nordic region, in time span over 100 years.
    The project contractors were NORDKLIM/NORDMET on behalf of the National 
    meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), 
    Norway (DNMI) and Sweden (SMHI).

An integrated set of functions for the analysis of 
  multivariate normal datasets with missing values, including implementation of
  the EM algorithm, data augmentation, and multiple imputation.

Provides standardized access to a range of re-scaling methods for numerical vectors
    and time-series features calculated within the 'theft' ecosystem.

Package including an interactive Shiny application for
    testing normality visually.

Provides simple methods for centering and scaling of numeric data. 
    Columns or rows can be ignored when normalizing or be normalized jointly.

Normalize a given Hadamard matrix. A Hadamard matrix is said to be normalized when its first row and first column entries are all 1, see Hedayat, A. and Wallis, W. D. (1978) "Hadamard matrices and their applications. The Annals of Statistics, 1184-1238." <doi:10.1214/aos/1176344370>.

Functions for the normal Laplace distribution.  Currently, it
 provides limited functionality.  Density, distribution and quantile
 functions, random number generation, and moments are provided.

A collection of utilities referred to Exponential Power distribution, also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4).

The robustness of many of the statistical techniques, such as factor analysis, applied in 
          the social sciences rests upon the assumption of item-level normality. However, when dealing 
          with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964)
          <http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for 
          large datasets of continuous variables, its application in current software programs is cumbersome
          with analysts having to take several steps to normalise each variable. We present an R package 
          'normalr' that enables researchers to make convenient optimal transformations of multiple variables
          in datasets. This R package enables users to quickly and accurately: (1) anchor all of their 
          variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, 
          (3) apply each unique exponent to its variable, (4) rescale resultant values to within their 
          original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, 
          kurtosis, and other inferential assessments of normality.