Title: An Interface to the 'Power BI REST APIs'
Authors@R: person("Christian", "Vermehren", email = "cv@cantab.net",
Description: Makes it easy to push data to 'Power BI' using R and the 'Power BI
A set of functions for turning data frames into 'Power BI' datasets and
refreshing these datasets are provided. Administrative tasks such as
monitoring refresh statuses and pulling metadata about workspaces and users
Imports: data.table, jsonlite, httr, utils, AzureAuth

Title: Power Estimates for ANOVA Designs
Description: Functions for bootstrapping the power of ANOVA designs
based on estimated means and standard deviations of the conditions.
Please refer to the documentation of the boot.power.anova() function
Imports: lme4, gdata, MASS, reshape2, stringr, plyr, ggplot2
Packaged: 2016-04-05 15:46:13 UTC; fpapenmeier

Packaged: 2016-10-06 08:29:41 UTC; fpapenmeier

Packaged: 2018-04-04 13:36:28 UTC; fpapenmeier

Packaged: 2021-02-25 09:44:29 UTC; fpapenmeier

Title: Extensions of 'dplyr' and 'fuzzyjoin' Join Functions
Description: Extensions of 'dplyr' and 'fuzzyjoin' join functions. Packed with
features to preprocess the data, apply various data checks, and deal with
Imports: dplyr, generics, glue, rlang, tidyselect, vctrs, purrr,
magrittr, lifecycle, tibble, tidyr, cli, methods
David Robinson [ctb] (aut of fuzzyjoin),

Description: We extend 'dplyr' and 'fuzzyjoin' join functions with
Imports: dplyr, glue, rlang, tidyselect, vctrs, purrr, tibble, tidyr,

Title: Power Calculations for Longitudinal Multilevel Models
Authors@R: person("Kristoffer", "Magnusson", email = "hello@kristoffer.email",
Description: Calculate power for two- and three-level multilevel longitudinal
studies with missing data. Both the third-level factor (e.g. therapists,
schools, or physicians), and the second-level factor (e.g. subjects),
can be assigned random slopes. Studies with partially nested designs,
unequal cluster sizes, unequal allocation to treatment arms, and different
dropout patterns per treatment are supported. For all designs power can be
calculated both analytically and via simulations. The analytical
calculations extends the method described in Galbraith et al. (2002)
Additionally, the simulation tools provides flexible ways to investigate
bias, type I errors and the consequences of model misspecification.
Imports: stats, lmerTest (>= 2.0), lme4 (>= 1.1), ggplot2 (>= 2.2),
ggsci, pbmcapply (>= 1.1), Matrix, MASS, gridExtra, scales,
Suggests: dplyr, tidyr, knitr, rmarkdown, viridis, shiny (>= 1.0),