Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.

Various utilities for the Multiplicative Multinomial distribution.

Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.

The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset, A. & Mary-Huard, T. (2022) <doi:10.1371/journal.pcbi.1009659>).

Used for general multiple mediation analysis. 
	The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.

Used for general multiple mediation analysis with big data sets.

Contains the data sets for the first and second editions of the textbook 
    "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. 
    The first edition of the book was published by Oxford University Press in 2018 with 
    ISBN-13: 978-019882472. The second edition is expected to be published in January 
    2027.

A formula-driven framework for maximizing target functions
    via the minorization-maximization (MM) algorithm. The package
    represents the target as a symbolic expression tree, infers its
    curvature via disciplined-convex-programming rules, and constructs
    a separable surrogate at each iterate using only Jensen's
    inequality and the supporting hyperplane. The driver maximizes the
    surrogate via block-coordinate Newton with line search, falling
    back to a multivariate step on any non-separable residue. A
    formula interface accepts standard R expressions (including
    `sum()` reductions and `X %*% theta` design-matrix products) so
    statistical models such as Poisson regression can be written in
    one line.