Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881.

The D-score summarizes a child's performance on developmental milestones
    into a single number. Its key feature is its generic nature. The method
    does not depend on a specific measurement instrument. The statistical
    method underlying the D-score is described in van Buuren et al. (2025)
    <doi:10.1177/01650254241294033>. This package implements model keys to
    convert milestone scores to D-scores; maps instrument-specific item names
    to a generic 9-position naming convention; computes D-scores and their
    precision from a child's milestone scores; and converts D-scores to
    Development-for-Age Z-scores (DAZ) using age-conditional reference
    standards.

Implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) <doi:10.1038/s41598-024-54149-y>.

A toolbox for distributed dynamic regression modeling,
    parallel estimation, multiple distributed sampling algorithms (Metropolis-Hastings,
    block bootstrap, adaptive, hypergeometric), sparse matrix
    optimization, model visualization, prediction and performance
    evaluation.  The philosophy of the package is described in Guo (2025)
    <doi:10.1038/s41598-025-93333-6>.

Applies dynamic structural equation models to time-series data
	with generic and simplified specification for simultaneous and lagged
	effects. Methods are described in Thorson et al. (2024)
	"Dynamic structural equation models synthesize ecosystem dynamics 
	constrained by ecological mechanisms."

Provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.