Wrapper around various existing tools and command-line interfaces,
  providing a standard interface, simple parallelization, and detailed logging.
  For microarray data, maps probe sets to standard gene IDs, building on
  'GEOquery' Davis and Meltzer (2007) <doi:10.1093/bioinformatics/btm254>,
  'ArrayExpress' Kauffmann et al. (2009) <doi:10.1093/bioinformatics/btp354>,
  Robust multi-array average 'RMA' Irizarry et al. (2003) <doi:10.1093/biostatistics/4.2.249>,
  and 'BrainArray' Dai et al. (2005) <doi:10.1093/nar/gni179>.
  For RNA-seq data, fetches metadata and raw reads from National Center for Biotechnology
  Information (NCBI) Sequence Read Archive (SRA), performs standard adapter and
  quality trimming using 'TrimGalore' Krueger <https://github.com/FelixKrueger/TrimGalore>,
  performs quality control checks using 'FastQC' Andrews <https://github.com/s-andrews/FastQC>,
  quantifies transcript abundances using 'salmon' Patro et al. (2017) <doi:10.1038/nmeth.4197> and potentially
  'refgenie' Stolarczyk et al. (2020) <doi:10.1093/gigascience/giz149>,
  aggregates the results using 'MultiQC' Ewels et al. (2016) <doi:10.1093/bioinformatics/btw354>,
  maps transcripts to genes using 'biomaRt' Durinkck et al. (2009) <doi:10.1038/nprot.2009.97>,
  and summarizes transcript-level quantifications for gene-level analyses using
  'tximport' Soneson et al. (2015) <doi:10.12688/f1000research.7563.2>.

A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.