'associate.R' 'crowding_distance.R' 'data.R'
'generate_reference_points.R' 'geneticoperator.R'
'get_fixed_rowsum_integer_matrix.R' 'miscfun.R' 'niching.R'
'non_dominated_fronts.R' 'numberOrNAOrMatrix-class.R'
'nsga-class.R' 'nsga.R' 'nsga2-class.R' 'nsga2.R'
'nsga3-class.R' 'nsga3.R' 'nsgaControl.R'
'reference_point_multi_layer.R' 'rmoo.R'
'performance_metrics.R' 'plotting.R' 'sharing.R'
'update_points.R' 'zzz.R'

The 'rmoo' package is a framework for multi- and many-objective
optimization, which allows researchers and users versatility
in parameter configuration, as well as tools for analysis, replication
and visualization of results. The 'rmoo' package was built as a fork of
the 'GA' package by Luca Scrucca(2017) <DOI:10.32614/RJ-2017-008> and
implementing the Non-Dominated Sorting Genetic Algorithms proposed
by K. Deb's.
'AllClasses.R' 'associate.R' 'crowding_distance.R' 'data.R'
'generate_reference_points.R' 'geneticoperator.R'
'get_fixed_rowsum_integer_matrix.R' 'miscfun.R' 'AllGenerics.R'
'niching.R' 'non_dominated_fronts.R' 'nsga.R' 'nsga2.R'
'nsga3.R' 'nsgaControl.R' 'performance_metrics.R'
'reference_point_multi_layer.R' 'rmoo.R' 'rmoo_func.R'
'sharing.R' 'update_points.R' 'zzz.R'
stats, utils, graphics, methods, GA, grDevices, ggplot2,
plotly
testthat, covr, rgl, ecr, emoa, cdata, dplyr, reshape2

Rounded Mixture Package. Performs probability mass function
estimation with Dirichlet process mixtures of rounded kernels
C code by A. Canale and N. Lunardon, port and R code by A.
Canale
Performs probability mass function estimation with
Dirichlet process mixtures of rounded kernels

Rounded Mixture Package. Performs Probability Mass Function
Estimation with Nonparametric Mixtures of Rounded Kernels
Antonio Canale [aut,cre], Nicola Lunardon [ctb]
Performs probability mass function estimation with nonparametric mixtures of rounded kernels.

Causal Mediation Analysis Using Weighting Approach
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions.