Imports: bvls, Matrix, RSEIS, pracma, geigen, fields
Description: Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, 'ART' and 'SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included.

Title: Probabilistic Sex Estimate using Logistic Regression, Based on
Description: An R-Shiny application implementing two methods for sexing the human os coxae based on eleven trichotomic traits, following Bruzek (2002) <doi:10.1002/ajpa.10012>.

Description: An R-Shiny application implementing a method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.
Authors@R: c(person(given="Frédéric", family="Santos", role=c("aut", "cre"), email="frederic.santos@u-bordeaux.fr", comment=c(ORCID="0000-0003-1445-3871")),
Packaged: 2019-05-13 12:29:52 UTC; pingouache
Author: Frédéric Santos [aut, cre] (<https://orcid.org/0000-0003-1445-3871>),

Authors@R: c(person(given="Frédéric", family="Santos", role=c("aut", "cre"),

Title: A Penalized EM algorithm incorporating missing-data mechanism
Author: Lin Chen <lchen@health.bsd.uchicago.edu> and Pei Wang <pwang@fhcrc.org>
Description: This package provides functions to perform multivariate Gaussian parameter estimation based on data with abundance-dependent missingness. It implements a penalized Expectation-Maximization (EM) algorithm. The package is tailored for but not limited to proteomics data applications, in which a large proportion of the data are often missing-not-at-random with lower values (or absolute values) more likely to be missing.

Title: Multivariate Analysis of Variance Based on Distances and
Author: Laura Vicente-Gonzalez, Jose Luis Vicente-Villardon
Description: Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.
Depends: R (>= 4.0.0), Matrix, xtable, MASS, scales, deldir, grDevices,