A Self-Adaptive Approach for Demodulating Multi-Component Signal
c(
person("Shubhra", "Prakash", email = "shubhraprakash279@gmail.com", role = c("trl","aut","cre")))
Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the 'Python' library 'PyLMD'.
knitr, rmarkdown, ggformula, testthat (>= 3.0.0)

An assortment of functions to facilitate modeling with linear
mixed-effects regression (LMER).
Antoine Tremblay, IWK Health Center <trea26@gmail.com>
The main functions are fixed effect back-fitting functions
(on F or t values and log-likelihood ratio testing) and a
random effect forward-fitting function (log-likelihood ratio
testing). Other functions include a function to compute ANOVAs
with upper-bound p-values (anti-conservative), a function to
graph model criticism plots, and another to trim data on model
residuals.

A suite of functions to back-fit fixed effects and forward-fit
random effects.
Functions to back-fit fixed effects (on F or t values and,
potentially, log-likelihood ratio testing) and to forward-fit
random effects (log-likelihood ratio testing). Note that the
back- and forward-fitting of logistic models is not yet
supported. The package also includes a function to compute
ANOVAs with upper- and lower-bound p-values (anti-conservative
and conservative, respectively), a function to graph model
criticism plots, functions to trim data on model residuals or
on a response variable (per subject), and a function to perform
posthoc analyses.

Functions to back-fit fixed effects (on F or t values and,
potentially, log-likelihood ratio testing) and to forward-fit
random effects (using log-likelihood ratio testing). Note that
the back- and forward-fitting of generalized linear
mixed-effects regression (\code{glmer}s) models is not yet
supported. The package also includes a function to compute
ANOVAs with upper- and lower-bound p-values (anti-conservative
and conservative, respectively), a function to graph model
criticism plots, functions to trim data on model residuals or
on a response variable (per subject), and a function to perform
posthoc analyses.