An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.

Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in <doi:10.48550/arXiv.2303.04754>) and has been found to outperform several other commonly applied estimators.

Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.

Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. 
    It can account for the correlated instruments and the horizontal pleiotropy, 
    and can provide the accurate estimates of both causal effect and horizontal 
    pleiotropy effect as well as the two corresponding p-values. There are two main 
    functions in the 'PPMR' package. One is PMR_individual() for individual level data, 
    the other is PMR_summary() for summary data.

A unified, computation-friendly framework for penalized principal
    machines (P2M), a class of sparse sufficient dimension reduction (SDR)
    estimators for regression and binary classification. Principal machines
    (PM) estimate the central subspace by solving a family of convex-loss
    problems over several cutoffs; their penalized counterparts (P2M) add a
    row-group sparsity penalty so that dimension reduction and variable
    selection are performed simultaneously. All estimators are fitted by a
    single group coordinate descent (GCD) algorithm that accommodates least
    squares, logistic, asymmetric least squares, L2-hinge, hinge (support
    vector machine, SVM) and quantile losses, together with the least absolute
    shrinkage and selection operator (LASSO), the smoothly clipped absolute
    deviation (SCAD) penalty and the minimax concave penalty (MCP). Methods are
    described in Li, Artemiou and Li (2011) <doi:10.1214/11-AOS932>, Shin and
    Artemiou (2017) <doi:10.1016/j.csda.2016.12.003>, Artemiou, Dong and Shin
    (2021) <doi:10.1016/j.patcog.2020.107768> and Breheny and Huang (2015)
    <doi:10.1007/s11222-013-9424-2>.