This package implements a novel multivariate meta-analysis.

Easy Computation of Marketing Metrics with Different Analysis
Axis
c(
person("Shinichi", "Takayanagi", , "shinichi.takayanagi@gmail.com", role = c("aut", "cre")),
person("Nagi", "Teramo", , "teramonagi@gmail.com", role = c("aut")),
person("Takahiro", "Yoshinaga", , "t.yoshinaga0106@gmail.com", role = c("aut"))
)
Provides a mechanism for easy computation of marketing metrics.
By default in this package, metrics for digital marketing (e.g. CTR (Click Through Rate), CVR (Conversion Rate), CPC (Cost Per Click) etc) are calculated but you can define your own metrics easily.
In addition to that, you can change an analysis axis to calculate these metrics.
covr, devtools, testthat, knitr, rmarkdown
Shinichi Takayanagi [aut, cre],
Nagi Teramo [aut],
Takahiro Yoshinaga [aut]

c(
person("Shinichi", "Takayanagi", , "shinichi.takayanagi@gmail.com", role = c("aut", "cre")),
person("Nagi", "Teramo", , "teramonagi@gmail.com", role = c("aut")),
person("Takahiro", "Yoshinaga", , "t.yoshinaga0106@gmail.com", role = c("aut")),
person("Yanjin", "Li", , "yanjin0501@gmail.com", role = c("aut"))
)
magrittr, dplyr, purrr, stringr, rlang, ggplot2
'data.R' 'disaggregate.R' 'mmetrics.R' 'metrics_definition.R'
'mplot.R'
Shinichi Takayanagi [aut, cre],
Nagi Teramo [aut],
Takahiro Yoshinaga [aut],
Yanjin Li [aut]

Mixed-mode latent class regression (also known as mixed-mode
mixture model regression or mixed-mode mixture regression models) which
can handle both longitudinal and one-time responses, although it is created
with longitudinal data in mind.

Multivariate Marginal Models with Response Specific Parameters
fits multivariate marginal models with response specific
parameters for multivariate longitudinal data for both
continuous and discrete responses

fits multivariate marginal models for multivariate
longitudinal data for both continuous and discrete responses

an R package for analyzing multivariate longitudinal data with
multivariate marginal models