Parametric modeling of M-quantile regression coefficient functions.

Provides functions for actuarial risk modeling, including survival models,
    life annuities, multiple-decrement models, and mortality improvement projections.
    The package is designed to align with standard actuarial notation and supports
    teaching, exam preparation, and reproducible actuarial analysis. The methods are
    based on standard actuarial references including Camilli, Duncan and London (2014,
    ISBN:9781625423474) "Models for Quantifying Risk" and Dickson, Hardy and Waters
    (2020, ISBN:9781108478083) "Actuarial Mathematics for Life Contingent Risks".

Provides an implementation of methods for multivariate 
    multiple regression with adaptive shrinkage priors as
    described in F. Morgante et al (2023) <doi:10.1371/journal.pgen.1010539>.

The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.

Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, 'MR.RGM' extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. 'MR.RGM' facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, 'MR.RGM' provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0).