Imports: tibble, dplyr, messydates, lubridate, purrr, rlang, stringr,
usethis, jsonlite, remotes, httr, progress, ggplot2

Imports: tibble, dplyr, messydates, purrr, rlang, stringr, usethis,
jsonlite, remotes, httr, ggplot2 (>= 3.4.0), migraph, cshapes,
Packaged: 2022-11-11 10:27:19 UTC; henriquesposito

Packaged: 2022-11-17 15:45:30 UTC; henriquesposito

Description: Frequently one needs a convenient way to build and tune,
several models in one go.The goal is to provide a number of convenience functions useful in machine learning
applications. It provides the ability to build,tune and obtain predictions of
several models in one function. The models are built using 'caret' functions with
Packaged: 2019-02-22 11:33:57 UTC; Lance Nelson

Description: Frequently one needs a convenient way to build and tune
applications. It provides the ability to build, tune and obtain predictions of
Packaged: 2019-07-04 07:27:47 UTC; Lance Nelson

Packaged: 2020-02-05 16:14:47 UTC; Lance Nelson

Packaged: 2020-02-12 04:54:48 UTC; Lance Nelson

several models in one go.The goal is to provide a number of machine learning convenience
functions. It provides the ability to build, tune and obtain predictions of

several models in one function. The models are built using functions from
Suggests: knitr, rmarkdown, testthat, covr, markdown

Title: Mediation, Moderation and Moderated-Mediation After Model
Description: Computes indirect effects, conditional effects, and conditional
indirect effects in a structural equation model or path model after model
fitting, with no need to define any user parameters or label any paths in
the model syntax. Can also form bootstrap confidence intervals by doing
bootstrapping only once and reusing the bootstrap estimates in all
subsequent computations. Supports bootstrap confidence intervals for
standardized (partially or completely) indirect effects, conditional
effects, and conditional indirect effects as described in Cheung (2009)
<doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui, and Vong (2022)
<doi:10.1037/hea0001188>. Model fitting can be done by structural equation
modeling using lavaan() or regression using lm().
Suggests: knitr, rmarkdown, semPlot, semptools, testthat (>= 3.0.0)
Imports: lavaan, boot, parallel, pbapply, stats, ggplot2
Author: Shu Fai Cheung [aut, cre] (<https://orcid.org/0000-0002-9871-9448>),
Maintainer: Shu Fai Cheung <shufai.cheung@gmail.com>