Group Subset Selection (groupsubsetselection)
Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.

Tools for Standardizing Variables for Regression in R (standardize)
Tools which allow regression variables to be placed on similar scales, offering computational benefits as well as easing interpretation of regression output.

Provides Docstring Capabilities to R Functions (docstring)
Provides the ability to display something analogous to Python’s docstrings within R. By allowing the user to document their functions as comments at the beginning of their function without requiring putting the function into a package we allow more users to easily provide documentation for their functions. The documentation can be viewed just like any other help files for functions provided by packages as well.

Price and Quantity Indices (micEconIndex)
Tools for calculating Laspeyres, Paasche, and Fisher price and quantity indices.

Scale Space Multiresolution Analysis of Random Signals (mrbsizeR)
A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features. mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales. To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011>.