Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.

Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables.
    Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model,
    respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. 
    This model can be employed in social and behavioral sciences, economy and finance, and  genomics, 
    to extract interpretable nonlinear factors. More details can be referred to 
    Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2021) <doi:10.1080/01621459.2021.1999818>.

Download 'Google' fonts and generate 'CSS' to use in 'rmarkdown' documents and 
  'shiny' applications. Some popular fonts are included and ready to use.

Implements the parametric g-formula algorithm of Robins (1986) 
    <doi:10.1016/0270-0255(86)90088-6>. The g-formula can be used to estimate 
    the causal effects of hypothetical time-varying treatment interventions on 
    the mean or risk of an outcome from longitudinal data with time-varying 
    confounding. This package allows: 1) binary or continuous/multi-level 
    time-varying treatments; 2) different types of outcomes (survival or 
    continuous/binary end of follow-up); 3) data with competing events or 
    truncation by death and loss to follow-up and other types of censoring 
    events; 4) different options for handling competing events in the case of 
    survival outcomes; 5) a random measurement/visit process; 6) joint 
    interventions on multiple treatments; and 7) general incorporation of a 
    priori knowledge of the data structure.

Implements the G-Formula method for causal inference with time-varying treatments and
  confounders using Bayesian multiple imputation methods, as described by
  Bartlett, Olarte Parra and Daniel (2023) <arXiv:2301.12026>. It creates multiple synthetic imputed
  datasets under treatment regimes of interest using the 'mice' package. These can then be analysed
  using rules developed for analysing multiple synthetic datasets.