Help for package multitool

Title:

Run Multiverse Style Analyses

Version:

0.1.4

Description:

Run the same analysis over a range of arbitrary data processing decisions. 'multitool' provides an interface for creating alternative analysis pipelines and turning them into a grid of all possible pipelines. Using this grid as a blueprint, you can model your data across all possible pipelines and summarize the results.

License:

MIT + file LICENSE

Imports:

clipr, correlation, DiagrammeR, dplyr, flextable, furrr, future, ggdist, glue, ggplot2, moments, purrr, rlang, stringr, tibble, tidyr, lme4, parameters, performance

Suggests:

knitr, rmarkdown, testthat (≥ 3.0.0), tidyverse

Config/testthat/edition:

Encoding:

UTF-8

RoxygenNote:

7.3.1

VignetteBuilder:

knitr

URL:

https://ethan-young.github.io/multitool/, https://github.com/ethan-young/multitool

BugReports:

https://github.com/ethan-young/multitool/issues

Depends:

R (≥ 4.1.0)

NeedsCompilation:

Packaged:

2024-02-08 03:08:53 UTC; ethanyoung

Author:

Ethan Young

[aut, cre, cph], Stefan Vermeent

[aut]

Maintainer:

Ethan Young <young.ethan.scott@gmail.com>

Repository:

CRAN

Date/Publication:

2024-02-08 17:40:02 UTC

multitool: Run Multiverse Style Analyses

Description

Author(s)

Maintainer: Ethan Young young.ethan.scott@gmail.com (ORCID) [copyright holder]

Authors:

Stefan Vermeent p.c.s.vermeent@gmail.com (ORCID)

Add correlations from the `correlation` package in `easystats`

Description

Add correlations from the correlation package in easystats

Usage

add_correlations(
  .df,
  var_set,
  variables,
  focus_set = NULL,
  method = "auto",
  redundant = TRUE,
  add_matrix = TRUE
)

Arguments

.df

the original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

var_set

character string. Should be a descriptive name of the correlation matrix.

variables

the variables for which you would like to correlations. These variables will be passed to link[correlation]{correlation}. You can also use tidyselect to select variables.

focus_set

variables to focus one in a table. This produces a table where rows are each focused variables and columns are all other variables

method

a valid method of correlation supplied to link[correlation]{correlation} (e.g., 'pearson' or 'kendall'). Defaults to 'auto'. See link[correlation]{correlation} for more details.

redundant

logical, should the result include repeated correlations? Defaults to TRUE See link[correlation]{correlation} for details.

add_matrix

logical, add a traditional correlation matrix to the output. Defaults to TRUE.

Value

a data.framewith three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_correlations("predictors", matches("iv|mod|cov"), focus_set = c(cov1,cov2))

Add filtering/exclusion criteria to a multiverse pipeline

Description

Add filtering/exclusion criteria to a multiverse pipeline

Usage

add_filters(.df, ...)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

...

logical expressions to be used with filter separated by commas. Expressions should not be quoted.

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5)

Add a model and formula to a multiverse pipeline

Description

Add a model and formula to a multiverse pipeline

Usage

add_model(.df, model_desc, code)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

model_desc

a human readable name you would like to give the model.

code

literal model syntax you would like to run. You can use glue inside formulas to dynamically generate variable names based on a variable grid. For example, if you make variable grid with two versions of your IVs (e.g., iv1 and iv2), you can write your formula like so: lm(happiness ~ {iv} + control_var). The only requirement is that the variables written in the formula actually exist in the underlying data. You are also responsible for loading any packages that run a particular model (e.g., lme4 for mixed-models)

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess("scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_model("linear model", lm({dvs} ~ {ivs} * {mods}))

Add parameter keys names for later use in summarizing model effects

Description

Add parameter keys names for later use in summarizing model effects

Usage

add_parameter_keys(.df, parameter_group, parameter_name)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

parameter_group

character, a name for the parameter of interest

parameter_name

quoted or unquoted names of variables involved in a particular parameter of interest. Usually this is just a variable in your model (e.g., a main effect of your iv). However, it could also be an interaction term or some other term. You can use glue syntax to specify an effect that might use alternative versions of the same variable.

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_model("linear model", lm({dvs} ~ {ivs} * {mods})) |>
  add_parameter_keys("my_interaction", "{ivs}:{mods}") |>
  add_parameter_keys("my_main_effect", {ivs})

Add arbitrary postprocessing code to a multiverse pipeline

Description

Add arbitrary postprocessing code to a multiverse pipeline

Usage

add_postprocess(.df, postprocess_name, code)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

postprocess_name

a character string. A descriptive name for what the postprocessing step accomplishes.

code

the literal code you would like to execute after each analysis.

The code should be written to work with pipes (i.e., |> or %>%). Because the post-processing code comes last in each multiverse analysis step, the chosen model object will be passed to the post-processing code.

For example, if you fit a simple linear model like: lm(y ~ x1 + x2), and your post-processing code executes a call to anova, you would simply pass anova() to add_postprocess(). The underlying code would be:

data |> filters |> lm(y ~ x1 + x2, data = _) |> anova()

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess("scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_model("linear model", lm({dvs} ~ {ivs} * {mods})) |>
  add_postprocess("analysis of variance", aov())

Add arbitrary preprocessing code to a multiverse analysis pipeline

Description

Add arbitrary preprocessing code to a multiverse analysis pipeline

Usage

add_preprocess(.df, process_name, code)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

process_name

a character string. A descriptive name for what the preprocessing step accomplishes.

code

the literal code you would like to execute after data are filtered. glue syntax is allowed. An example might be centering or scaling a predictor after the appropriate filters are applied to the data.

The code should be written to work with pipes (i.e., |> or %>%). Pre-processing code will eventually take the base data along with any filters applied to the data. This means mutate calls are the most natural but other functions that take a data.frame as the first argument should work as well (as long as they also return a data.frame).

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess("scale_iv", 'mutate({ivs} = scale({ivs}))')

Add item reliabilities to a multiverse pipeline

Description

Add item reliabilities to a multiverse pipeline

Usage

add_reliabilities(.df, scale_name, items)

Arguments

.df

the original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

scale_name

a character string. Indicates the name of the scale or measure measured by the items or indicators in items.

items

the items (variables) that comprise a scale or measure. These variables will be passed to link[performance]{cronbachs_alpha}, link[performance]{item_intercor}, and link[performance]{item_reliability}. You can also use tidyselect to select variables.

Value

Examples


library(tidyverse)
library(multitool)

the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_reliabilities("unp_scale", c(iv1,iv2,iv3))

Add a set of descriptive statistics to compute over a set of variables

Description

Add a set of descriptive statistics to compute over a set of variables

Usage

add_summary_stats(.df, var_set, variables, stats)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

var_set

a character string. A name for the set of summary statistics

variables

the variables for which you would like to compute summary statistics. You can also use tidyselect to select variables.

stats

a character vector of stat names (e.g., c("mean","sd")). You are responsible for loading any packages that compute your preferred summary statistics. Summary statistic functions must work inside summarize.

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess(process_name = "scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_preprocess(process_name = "scale_mod", mutate({mods} := scale({mods}))) |>
  add_summary_stats("iv_stats", starts_with("iv"), c("mean", "sd")) |>
  add_summary_stats("dv_stats", starts_with("dv"), c("skewness", "kurtosis"))

Add a set of variable alternatives to a multiverse pipeline

Description

Add a set of variable alternatives to a multiverse pipeline

Usage

add_variables(.df, var_group, ...)

Arguments

.df

The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.

var_group

a character string. Indicates the name of the current set. For example, "primary_iv" could indicate this set are alternatives of the main predictor in an analysis.

...

the bare unquoted names of the variables to include as alternative options for this variable set. You can also use tidyselect to select variables.

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
 add_variables("ivs", iv1, iv2, iv3) |>
 add_variables("dvs", dv1, dv2) |>
 add_variables("mods", starts_with("mod"))

Summarize multiverse parameters

Description

Summarize multiverse parameters

Usage

condense(.unpacked, .what, .how, .group = NULL, list_cols = TRUE)

Arguments

.unpacked

an unpacked (with reveal or unnest) multiverse dataset.

.what

a specific column to summarize. This could be a model estimate, a summary statistic, correlation, or any other estimate computed over the multiverse.

.how

a named list. The list should contain summary functions (e.g., mean or median) the user would like to compute over the individual estimates from the multiverse

.group

an optional variable to group the results. This argument is passed directly to the .by argument used in dplyr::across

list_cols

logical, whether to create list columns for the raw values of any summarized columns. Useful for creating visualizations and tables. Default is TRUE.

Value

a summarized tibble containing a column for each summary method from .how

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_model("linear_model", lm({dvs} ~ {ivs} * {mods} + cov1))

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- run_multiverse(pipeline_grid[1:10,])

# Reveal and condense
the_multiverse |>
  reveal_model_parameters() |>
  filter(str_detect(parameter, "iv")) |>
  condense(coefficient, list(mean = mean, median = median))

Create a Analysis Pipeline diagram

Description

Create a Analysis Pipeline diagram

Usage

create_blueprint_graph(
  .pipeline,
  splines = "line",
  render = TRUE,
  show_code = FALSE,
  ...
)

Arguments

.pipeline

a data.frame produced by calling a series of add_* functions.

splines

options for how to draw edges (lines) for a grViz diagram

render

whether to render the graph or just output grViz code

show_code

whether to show the code that generated the diagram

...

additional options passed to DiagrammeR::grViz()

Value

grViz graph of your pipeline

Examples

library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(include1 == 0, include2 != 3, include3 > -2.5) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

create_blueprint_graph(full_pipeline)

Detect total number of analysis pipelines

Description

Detect total number of analysis pipelines

Usage

detect_multiverse_n(.pipeline, include_models = TRUE)

Arguments

.pipeline

a data.frame produced by calling a series of add_* functions.

include_models

Whether to count alternative models if you have more than one add_model() call.

Value

a numeric, the total number of unique analysis pipelines

Examples

library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(include1 == 0, include2 != 3, include3 > -2.5) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

detect_multiverse_n(full_pipeline)

Detect total number of filtering expressions your pipelines

Description

Detect total number of filtering expressions your pipelines

Usage

detect_n_filters(.pipeline)

Arguments

.pipeline

a data.frame produced by calling a series of add_* functions.

Value

a numeric, the total number of filtering expressions

Examples

library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(include1 == 0, include2 != 3, include3 > -2.5) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

detect_n_filters(full_pipeline)

Detect total number of models in your pipelines

Description

Detect total number of models in your pipelines

Usage

detect_n_models(.pipeline)

Arguments

.pipeline

a data.frame produced by calling a series of add_* functions.

Value

a numeric, the total number of unique models

Examples

library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(include1 == 0, include2 != 3, include3 > -2.5) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

detect_n_models(full_pipeline)

Detect total number of variable sets in your pipelines

Description

Detect total number of variable sets in your pipelines

Usage

detect_n_variables(.pipeline)

Arguments

.pipeline

a data.frame produced by calling a series of add_* functions.

Value

a numeric, the total number of unique variable sets

Examples

library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(include1 == 0, include2 != 3, include3 > -2.5) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

detect_n_variables(full_pipeline)

Expand a set of multiverse decisions into all possible combinations

Description

Expand a set of multiverse decisions into all possible combinations

Usage

expand_decisions(.pipeline)

Arguments

.pipeline

a data.frame produced by calling a series of add_* functions.

Value

a nested data.frame containing all combinations of arbitrary decisions for a multiverse analysis. Decision types will become list columns matching the type of decisions called along the pipeline (e.g., filters, variables, etc.). Any decisions containing glue syntax will be populated with the relevant information.

Examples


library(tidyverse)
library(multitool)

the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess(process_name = "scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_preprocess(process_name = "scale_mod", mutate({mods} := scale({mods}))) |>
  add_summary_stats("iv_stats", starts_with("iv"), c("mean", "sd")) |>
  add_summary_stats("dv_stats", starts_with("dv"), c("skewness", "kurtosis")) |>
  add_correlations("predictors", matches("iv|mod|cov"), focus_set = c(cov1,cov2)) |>
  add_correlations("outcomes", matches("dv|mod"), focus_set = matches("dv")) |>
  add_reliabilities("unp_scale", c(iv1,iv2,iv3)) |>
  add_model("no covariates", lm({dvs} ~ {ivs} * {mods})) |>
  add_model("with covariates", lm({dvs} ~ {ivs} * {mods} + cov1)) |>
  add_postprocess("aov", aov())

pipeline_expanded <- expand_decisions(full_pipeline)

Reveal the contents of a multiverse analysis

Description

Reveal the contents of a multiverse analysis

Usage

reveal(.multi, .what, .which = NULL, .unpack_specs = "no")

Arguments

.multi

a multiverse list-column tibble produced by run_multiverse.

.what

the name of a list-column you would like to unpack

.which

any sub-list columns you would like to unpack

.unpack_specs

character, options are "no", "wide", or "long". "no" (default) keeps specifications in a list column, wide unnests specifications with each specification category as a column. "long" unnests specifications and stacks them into long format, which stacks specifications into a decision_set and alternatives columns. This is mainly useful for plotting.

Value

the unnested part of the multiverse requested. This usually contains the particular estimates or statistics you would like to analyze over the decision grid specified.

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_model("linear_model", lm({dvs} ~ {ivs} * {mods} + cov1))

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- run_multiverse(pipeline_grid[1:10,])

# Reveal results of the linear model
the_multiverse |> reveal(model_fitted, model_parameters)

Reveal a set of multiverse correlations

Description

Reveal a set of multiverse correlations

Usage

reveal_corrs(.descriptives, .which, .unpack_specs = "no")

Arguments

.descriptives

a descriptive multiverse list-column tibble produced by run_descriptives.

.which

the specific name of the correlations requested

.unpack_specs

Value

an unnested set of correlations per decision from the multiverse.

Examples


library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(
    include1 == 0,
    include2 != 3,
    include2 != 2,
    include3 > -2.5,
    include3 < 2.5,
    between(include3, -2.5, 2.5)
  ) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_correlations("predictors", starts_with("iv")) |>
  add_summary_stats("iv_stats", starts_with("iv"), c("mean", "sd")) |>
  add_reliabilities("vio_scale", starts_with("iv")) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

my_descriptives <- run_descriptives(full_pipeline)

my_descriptives |>
  reveal_corrs(predictors_rs)

Reveal any messages about your models during a multiverse analysis

Description

Reveal any messages about your models during a multiverse analysis

Usage

reveal_model_messages(.multi, .unpack_specs = "no")

Arguments

.multi

a multiverse list-column tibble produced by run_multiverse.

.unpack_specs

Value

the unnested model messages captured during analysis.

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_model("linear_model", lm({dvs} ~ {ivs} * {mods} + cov1))

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- run_multiverse(pipeline_grid[1:10,])

# Reveal results of the linear model
the_multiverse |>
  reveal_model_messages()

Reveal the model parameters of a multiverse analysis

Description

Reveal the model parameters of a multiverse analysis

Usage

reveal_model_parameters(.multi, parameter_key = NULL, .unpack_specs = "no")

Arguments

.multi

a multiverse list-column tibble produced by run_multiverse.

parameter_key

character, if you added parameter keys to your pipeline, you can specify if you would like filter the parameters using one of your parameter keys. This is useful when different variables are being switched out across the multiverse but represent the same effect of interest.

.unpack_specs

Value

the unnested model paramerters from the multiverse.

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_model("linear_model", lm({dvs} ~ {ivs} * {mods} + cov1))

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- run_multiverse(pipeline_grid[1:10,])

# Reveal results of the linear model
the_multiverse |>
  reveal_model_parameters()

Reveal the model performance/fit indices from a multiverse analysis

Description

Reveal the model performance/fit indices from a multiverse analysis

Usage

reveal_model_performance(.multi, .unpack_specs = "no")

Arguments

.multi

a multiverse list-column tibble produced by run_multiverse.

.unpack_specs

Value

the unnested model performance/fit indices from a multiverse analysis.

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_model("linear_model", lm({dvs} ~ {ivs} * {mods} + cov1))

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- run_multiverse(pipeline_grid[1:10,])

# Reveal results of the linear model
the_multiverse |>
  reveal_model_performance()

Reveal any warnings about your models during a multiverse analysis

Description

Reveal any warnings about your models during a multiverse analysis

Usage

reveal_model_warnings(.multi, .unpack_specs = "no")

Arguments

.multi

a multiverse list-column tibble produced by run_multiverse.

.unpack_specs

Value

the unnested model warnings captured during analysis

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_model("linear_model", lm({dvs} ~ {ivs} * {mods} + cov1))

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- run_multiverse(pipeline_grid[1:10,])

# Reveal results of the linear model
the_multiverse |>
  reveal_model_warnings()

Reveal a set of multiverse cronbach's alpha statistics

Description

Reveal a set of multiverse cronbach's alpha statistics

Usage

reveal_reliabilities(.descriptives, .which, .unpack_specs = "no")

Arguments

.descriptives

a descriptive multiverse list-column tibble produced by run_descriptives.

.which

the specific name of the alphas

.unpack_specs

Value

an unnested set of correlations per decision from the multiverse.

Examples


library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(
    include1 == 0,
    include2 != 3,
    include2 != 2,
    include3 > -2.5,
    include3 < 2.5,
    between(include3, -2.5, 2.5)
  ) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_correlations("predictor correlations", starts_with("iv")) |>
  add_summary_stats("iv_stats", starts_with("iv"), c("mean", "sd")) |>
  add_reliabilities("vio_scale", starts_with("iv")) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

my_descriptives <- run_descriptives(full_pipeline)

my_descriptives |>
  reveal_reliabilities(vio_scale_alpha)

Reveal a set of summary statistics from a multiverse analysis

Description

Reveal a set of summary statistics from a multiverse analysis

Usage

reveal_summary_stats(.descriptives, .which, .unpack_specs = "no")

Arguments

.descriptives

a descriptive multiverse list-column tibble produced by run_descriptives.

.which

the specific name of the summary statistics

.unpack_specs

Value

an unnested set of summary statistics per decision from the multiverse.

Examples


library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(
    include1 == 0,
    include2 != 3,
    include2 != 2,
    include3 > -2.5,
    include3 < 2.5,
    between(include3, -2.5, 2.5)
  ) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_correlations("predictor correlations", starts_with("iv")) |>
  add_summary_stats("iv_stats", starts_with("iv"), c("mean", "sd")) |>
  add_reliabilities("vio_scale", starts_with("iv")) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

my_descriptives <- run_descriptives(full_pipeline)

my_descriptives |>
  reveal_summary_stats(iv_stats)

Run a multiverse-style descriptive analysis based on a complete decision grid

Description

Run a multiverse-style descriptive analysis based on a complete decision grid

Usage

run_descriptives(.pipeline, show_progress = TRUE)

Arguments

.pipeline

a tibble produced by a series of add_* calls. Importantly, this needs to be a pre-expanded pipeline because descriptive analyses only change when the underlying cases change. Thus, only filtering decisions will be used and internally expanded before calculating various descriptive analyses.

show_progress

logical, whether to show a progress bar while running.

Value

single tibble containing tidied results for all descriptive analyses specified

Examples


library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_summary_stats("iv_stats", starts_with("iv"), c("mean", "sd")) |>
  add_summary_stats("dv_stats", starts_with("dv"), c("skewness", "kurtosis")) |>
  add_correlations("predictors", matches("iv|mod|cov"), focus_set = c(cov1,cov2)) |>
  add_correlations("outcomes", matches("dv|mod"), focus_set = matches("dv")) |>
  add_reliabilities("unp_scale", c(iv1,iv2,iv3)) |>
  add_reliabilities("vio_scale", starts_with("mod"))

run_descriptives(full_pipeline)

Run a multiverse based on a complete decision grid

Description

Run a multiverse based on a complete decision grid

Usage

run_multiverse(.grid, ncores = 1, save_model = FALSE, show_progress = TRUE)

Arguments

.grid

a tibble produced by expand_decisions

ncores

numeric. The number of cores you want to use for parallel processing.

save_model

logical, indicates whether to save the model object in its entirety. The default is FALSE because model objects are usually large and under the hood, parameters and performance is used to summarize the most useful model information.

show_progress

logical, whether to show a progress bar while running.

Value

a single tibble containing tidied results for the model and any post-processing tests/tasks. For each unique test (e.g., an lm or aov called on an lm), a list column with the function name is created with parameters and performance and any warnings or messages printed while fitting the models. Internally, modeling and post-processing functions are checked to see if there are tidy or glance methods available. If not, summary will be called instead.

Examples

library(tidyverse)
library(multitool)

# Simulate some data
the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess(process_name = "scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_preprocess(process_name = "scale_mod", mutate({mods} := scale({mods}))) |>
  add_model("no covariates",lm({dvs} ~ {ivs} * {mods})) |>
  add_model("covariate", lm({dvs} ~ {ivs} * {mods} + cov1)) |>
  add_postprocess("aov", aov())

pipeline_grid <- expand_decisions(full_pipeline)

# Run the whole multiverse
the_multiverse <- run_multiverse(pipeline_grid[1:10,])

Show multiverse data code pipelines

Description

Each show_code* function should be self-explanatory - they indicate where along the multiverse pipeline to extract code. The goal of these functions is to create a window into each multiverse decision set context/results and allow the user to inspect specific decisions straight from the code that produced it.

Usage

show_code_filter(.grid, decision_num, copy = FALSE)

show_code_preprocess(.grid, decision_num, copy = FALSE)

show_code_model(.grid, decision_num, copy = FALSE)

show_code_postprocess(.grid, decision_num, copy = FALSE)

show_code_summary_stats(.grid, decision_num, copy = FALSE)

show_code_corrs(.grid, decision_num, copy = FALSE)

show_code_reliabilities(.grid, decision_num, copy = FALSE)

Arguments

.grid

a full decision grid created by expand_decisions.

decision_num

numeric. Indicates which 'universe' in the multiverse to show underlying code.

copy

logical. Whether to copy the pipeline code to the clipboard using write_clip. Defaults to FALSE.

Value

the code that generated results up to the specified point in an analysis pipeline. The code is printed in the console and can be optionally copied to the clipboard.

Functions

show_code_preprocess(): Show the code up to the preprocessing stage
show_code_model(): Show the code up to the modeling stage
show_code_postprocess(): Show the code up to the post-processing stage
show_code_summary_stats(): Show the code for computing summary statistics
show_code_corrs(): Show the code for computing correlations
show_code_reliabilities(): Show the code for computing scale reliability

Summarize samples sizes for each unique filtering expression

Description

Summarize samples sizes for each unique filtering expression

Usage

summarize_filter_ns(.pipeline)

Arguments

.pipeline

a data.frame produced by calling a series of add_* functions.

Value

a tibble with each row representing a filtering expression and four columns: filter_expression, variable, n_retained, and n_excluded.

Examples

library(tidyverse)
library(multitool)

# create some data
the_data <-
  data.frame(
    id  = 1:500,
    iv1 = rnorm(500),
    iv2 = rnorm(500),
    iv3 = rnorm(500),
    mod = rnorm(500),
    dv1 = rnorm(500),
    dv2 = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

# create a pipeline blueprint
full_pipeline <-
  the_data |>
  add_filters(include1 == 0, include2 != 3, include3 > -2.5) |>
  add_variables(var_group = "ivs", iv1, iv2, iv3) |>
  add_variables(var_group = "dvs", dv1, dv2) |>
  add_model("linear model", lm({dvs} ~ {ivs} * mod))

summarize_filter_ns(full_pipeline)

multitool: Run Multiverse Style Analyses

Description

Author(s)

See Also

Add correlations from the correlation package in easystats

Description

Usage

Arguments

Value

Examples

Add filtering/exclusion criteria to a multiverse pipeline

Description

Usage

Arguments

Value

Examples

Add a model and formula to a multiverse pipeline

Description

Usage

Arguments

Value

Examples

Add parameter keys names for later use in summarizing model effects

Description

Usage

Arguments

Value

Examples

Add arbitrary postprocessing code to a multiverse pipeline

Description

Usage

Arguments

Value

Examples

Add arbitrary preprocessing code to a multiverse analysis pipeline

Description

Usage

Arguments

Value

Examples

Add item reliabilities to a multiverse pipeline

Description

Usage

Arguments

Value

Examples

Add a set of descriptive statistics to compute over a set of variables

Description

Usage

Arguments

Value

Examples

Add a set of variable alternatives to a multiverse pipeline

Description

Usage

Arguments

Value

Examples

Summarize multiverse parameters

Description

Usage

Arguments

Value

Examples

Create a Analysis Pipeline diagram

Description

Usage

Arguments

Value

Examples

Detect total number of analysis pipelines

Description

Usage

Arguments

Value

Examples

Detect total number of filtering expressions your pipelines

Description

Usage

Arguments

Add correlations from the `correlation` package in `easystats`