We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). <doi:10.1561/2200000016>, Fan, J., & Lv, J. (2008) <doi:10.1111/j.1467-9868.2008.00674.x>, Li, C., & Li, H. (2008) <doi:10.1093/bioinformatics/btn081>, Tibshirani, R. (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Zhao, Y., & Luo, X. (2022) <doi:10.4310/21-sii673>, and Zou, H., & Hastie, T. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.

Penalized regression for generalized linear models for
  measurement error problems (aka. errors-in-variables). The package
  contains a version of the lasso (L1-penalization) which corrects
  for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). 
  It also contains an implementation of the Generalized Matrix Uncertainty 
  Selector, which is a version the (Generalized) Dantzig Selector for the 
  case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).

A suite of functions for performing mediation
    analysis with high-dimensional mediators. In addition to centralizing code 
    from several existing packages for high-dimensional mediation analysis, we 
    provide organized, well-documented functions for a handle of methods 
    which, though programmed their original authors, have not previously been 
    formalized into R packages or been made presentable for public use. The 
    methods we include cover a broad array of approaches and objectives, and are
    described in detail by both our companion manuscript---"Methods for 
    Mediation Analysis with High-Dimensional DNA Methylation Data: Possible 
    Choices and Comparison"---and the original publications that proposed them. 
    The specific methods offered by our package include the Bayesian sparse 
    linear mixed model (BSLMM) by Song et al. (2019); high-dimensional mediation
    analysis (HDMA) by Gao et al. (2019); high-dimensional multivariate 
    mediation (HDMM) by Chén et al. (2018); high-dimensional linear mediation 
    analysis (HILMA) by Zhou et al. (2020); high-dimensional mediation analysis
    (HIMA) by Zhang et al. (2016); latent variable mediation analysis (LVMA) by 
    Derkach et al. (2019); mediation by fixed-effect model (MedFix) by 
    Zhang (2021); pathway LASSO by Zhao & Luo (2022); principal component 
    mediation analysis (PCMA) by Huang & Pan (2016); and sparse principal 
    component mediation analysis (SPCMA) by Zhao et al. (2020). Citations for 
    the corresponding papers can be found in their respective functions.