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.

High-dimensional matrix factor models have drawn much attention in view of the fact that observations are usually well structured to be an array such as in macroeconomics and finance. In addition, data often exhibit heavy-tails and thus it is also important to develop robust procedures. We aim to address this issue by replacing the least square loss with Huber loss function. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's Frobenius norm, which leads to a weighted iterative projection approach to compute and learn the parameters and thereby named as Robust-Matrix-Factor-Analysis (RMFA), see the details in He et al. (2023)<doi:10.1080/07350015.2023.2191676>. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm (IHR), see the details in He et al. (2023) <arXiv:2306.03317>. In this package, we also provide the algorithm for alpha-PCA by Chen & Fan (2021) <doi:10.1080/01621459.2021.1970569>, the Projected estimation (PE) method by Yu et al. (2022)<doi:10.1016/j.jeconom.2021.04.001>. In addition, the methods for determining the pair of factor numbers are also given.