Expectile and quantile regression of models with nonlinear effects
  e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes
  as well as boosting; also supplies expectiles for common distributions.

Distributes samples in batches while making batches
    homogeneous according to their description. Allows for an arbitrary
    number of variables, both numeric and categorical. For quality control
    it provides functions to subset a representative sample.

Provides convenience functions for researching experiences 
    including user, customer, patient, employee, and other human experiences.
    It provides a suite of tools to simplify data exploration such as 
    benchmarking, comparing groups, and checking for differences. The outputs 
    translate statistical approaches in applied experience research to 
    human readable output.

Provides various statistical methods for
  designing and analyzing randomized experiments. One functionality
  of the package is the implementation of randomized-block and
  matched-pair designs based on possibly multivariate pre-treatment
  covariates. The package also provides the tools to analyze various
  randomized experiments including cluster randomized experiments,
  two-stage randomized experiments, randomized experiments with 
  noncompliance, and randomized experiments with missing data.

Contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at <https://osf.io/b78je>.

Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.

Three functional modules, including genetic features, differential expression analysis and non-additive expression analysis were integrated into the package. And the package is suitable for RNA-seq and small RNA sequencing data. Besides, two methods of non-additive expression analysis were provided. One is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the gene in hybrid offspring with the expression level in parents. For non-additive expression analysis of RNA-seq data, it is only applicable to hybrid offspring (including two sub-genomes) species for the time being.