The 'MIDASim' package is a microbiome data simulator for generating realistic microbiome datasets by adapting a user-provided template. It supports the controlled introduction of experimental signals-such as shifts in taxon relative abundances, prevalence, and sample library sizes-to create distinct synthetic populations under diverse simulation scenarios. For more details, see He et al. (2024) <doi:10.1186/s40168-024-01822-z>.

The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.

Methods and tools for mixed frequency time series data analysis.
    Allows estimation, model selection and forecasting for MIDAS regressions.

Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's  MIDAS.

Provides tools and demonstrates methods for working with individual 
  undergraduate student-level records (registrar's data) in 'R'. Tools include 
  filters for program codes, data sufficiency, and timely completion. Methods 
  include gathering blocs of records, computing quantitative metrics such as 
  graduation rate, and creating charts to visualize comparisons. 'midfieldr' 
  interacts with practice data provided in 'midfielddata', an R data package 
  available at <https://midfieldr.github.io/midfielddata/>. 
  'midfieldr' also interacts with the full MIDFIELD database for users who have 
  access. This work is supported by the US National Science Foundation through 
  grant numbers 1545667 and 2142087.