Methods for decomposing seasonal data: STR (a Seasonal-Trend 
  time series decomposition procedure based on Regression) and Robust STR. In 
  some ways, STR is similar to Ridge Regression and Robust STR can be related to 
  LASSO. They allow for multiple seasonal components, multiple linear covariates 
  with constant, flexible and seasonal influence. Seasonal patterns (for both 
  seasonal components and seasonal covariates) can be fractional and flexible 
  over time; moreover they can be either strictly periodic or have a more 
  complex topology. The methods provide confidence intervals for the estimated 
  components. The methods can also be used for forecasting.

Offers a suite of functions for converting to and from
    (atomic) vectors, matrices, data.frames, and (3D+) arrays as well
    as lists of these objects. It is an alternative to the base R
    as.<str>.<method>() functions (e.g., as.data.frame.array()) that
    provides more useful and/or flexible restructuring of R objects.
    To do so, it only works with common structuring of R objects (e.g.,
    data.frames with only atomic vector columns).

Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test.

Provides a framework for performing discrete (share-level) simulations of
  investment strategies. Simulated portfolios optimize exposure to an input signal subject
  to constraints such as position size and factor exposure. For background see L. Chincarini
  and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".

Message translation is often managed with 'po' files and the 'gettext' programme, 
    but sometimes another solution is needed. In contrast to 'po' files, a more flexible approach 
    is used as in the Fluent <https://projectfluent.org/> project with R Markdown snippets. 
    The key-value approach allows easier handling of the translated messages.