Packaged: 2020-05-22 18:06:48 UTC; zhaoruzhang

Title: Calculation and Visualization of the Impact Effect Size Measure
Description: A non-parametric effect size measure capturing changes in central tendency or shape of data distributions for feature selection preceding machine-learning. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.
Imports: AdaptGauss, RcppAlgos, caTools, matrixStats, parallelDist,

Description: A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.
Imports: AdaptGauss, RcppAlgos, pracma, matrixStats, parallelDist,

Imports: DataVisualizations, RcppAlgos, pracma, matrixStats,

Imports: DataVisualizations, Hmisc, pracma, matrixStats, parallelDist,

Imports: Hmisc, caTools, matrixStats, parallelDist, methods, stats,

Imports: caTools, matrixStats, parallelDist, methods, stats, graphics,

Title: Identifying Causal Effect for Multi-Component Intervention Using
Description: In this package, you can find two functions proposed in
Ding, Geng and Zhou (2011) to estimate direct and indirect
causal effects with randomization and multiple-component
intervention using instrumental variable method.

Author: Roger Pros, Isaac Subirana, Joan Vila.
Description: Import and export data from the most common statistical formats by using
R functions that guarantee the least loss of the data information, giving special
attention to the date variables and the labelled ones.

Suggests: shiny, shinyBS, shinythemes, compareGroups, foreign

Title: Analyzing Data Through of Percentage of Importance Indice and
c(person(given = "Germano Leao Demolin",
Description: The Percentage of Importance Indice (Percentage_I.I.) bases in magnitudes, frequencies, and distributions of occurrence of an event (DEMOLIN-LEITE, 2021) <http://scielo.sld.cu/pdf/cjas/v55n2/2079-3480-cjas-55-02-e10.pdf>. This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of loss or income gain, on the productive system. The Percentage_I.I. = [(ks1 x c1 x ds1)/SUM (ks1 x c1 x ds1) + (ks2 x c2 x ds2) + (ksn x cn x dsn)] x 100. key source (ks) is obtained using simple regression analysis and magnitude (abundance). Constancy (c) is SUM of occurrence of L.S. or S.S. on the samples (absence = 0 or presence = 1), and distribution source (ds) is obtained using chi-square test. This index has derivations: i.e., i) Loss estimates and solutions effectiveness and ii) Attention and non-attention levels (DEMOLIN-LEITE,2024) <DOI: 10.1590/1519-6984.253215>.
Author: Germano Leao Demolin Leite [aut]