Title: Words and Gestures to Words and Sentences Score Conversion
Version: 0.2.0
Description: Convert MacArthur-Bates Communicative Development Inventory Words and Gestures scores to would-be scores on Words and Sentences, based on modeling from the Stanford Wordbank https://wordbank.stanford.edu/. See Day et al. (2025) <doi:10.1111/desc.70036>.
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: R (≥ 3.5.0)
LazyData: true
Suggests: testthat (≥ 3.0.0), wordbankr (≥ 1.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-06-16 14:44:22 UTC; Trevor
Author: Trevor K.M. Day ORCID iD [cre, aut]
Maintainer: Trevor K.M. Day <trevor.day@georgetown.edu>
Repository: CRAN
Date/Publication: 2025-06-17 07:20:01 UTC

cdiWG2WS: Words and Gestures to Words and Sentences Score Conversion

Description

Convert MacArthur-Bates Communicative Development Inventory Words and Gestures scores to would-be scores on Words and Sentences, based on modeling from the Stanford Wordbank https://wordbank.stanford.edu/. See Day et al. (2025) doi:10.1111/desc.70036.

Author(s)

Maintainer: Trevor K.M. Day trevor.day@georgetown.edu (ORCID)

Predict category scores

Description

Predict category scores

Usage

cat_models_stripped

Format

An object of many linear models.

Linear models predicting WS category scores from WG score and age. The embedded data have been stripped from the object.

References

Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677-694. doi:10.1017/S0305000916000209

Predict Connecting Words scores (no age)

Description

Predict Connecting Words scores (no age)

Usage

cw_noage_stripped

Format

Linear model

A linear model predicting Connecting Words scores from other scores (no age). The embedded data have been stripped from the object.

References

Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677-694. doi:10.1017/S0305000916000209

Predict Connecting Words scores

Description

Predict Connecting Words scores

Usage

cw_stripped

Format

Linear model

A linear model predicting Connecting Words scores from other scores and age. The embedded data have been stripped from the objects.

References

Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677-694. doi:10.1017/S0305000916000209

WG dictionary: items, categories

Description

WG dictionary: items, categories

Usage

g_dict

Format

A data frame with 396 rows and 4 columns:

category

Name of category

item_definition

Item label, e.g. "baa baa"

item_id

Unique ID

item_kind

Type of item, only "word"

References

Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677-694. doi:10.1017/S0305000916000209

WS dictionary: items, categories

Description

WS dictionary: items, categories

Usage

s_dict

Format

A data frame with 680 rows and 4 columns:

category

Name of category

item_definition

Item label, e.g. "baa baa"

item_id

Unique ID

item_kind

Type of item, only "word"

References

Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677-694. doi:10.1017/S0305000916000209

Predict total score

Description

Predict total score

Usage

total_WG_to_WS_noage_stripped

Format

A linear model

A linear model predicting WS score from WG score (no age). The embedded data have been stripped from the object.

References

Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677-694. doi:10.1017/S0305000916000209

Predict total score (w/ age)

Description

Predict total score (w/ age)

Usage

total_WG_to_WS_stripped

Format

A linear model

A linear model predicting WS score from WG score and age. The embedded data have been stripped from the object.

References

Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677-694. doi:10.1017/S0305000916000209

Simulate WS category score from given WG score

Description

Take 22 WG scores and simulates WS scores for each one.

Usage

wg2ws_category_score(wg_table, age = NA, WG_total = NA, verbose = FALSE)

Arguments

wg_table

A 22-row table with the columns category and n. Includes Sounds and Connecting Words.

age

(Optional). Age in months. If unset, models not including age are used

WG_total

NA/numeric: In the case of ⁠in/inside⁠, the WG score model can be off-by-one. Out of so many items, this is negligible, but can be set explicitly here.

verbose

T/F: Be verbose.

Details

This function predicts simulated WS scores for each category score independently. If an age is not supplied, models not using age are used (less accurate than including age).

Value

New scores (data frame of 22 scores)

References

Day, T. K. M., Borovsky, A., Thal, D., & Elison, J. T. (2025). Modeling Longitudinal Trajectories of Word Production With the CDI. Developmental Science, 28(4), e70036. doi:10.1111/desc.70036

Examples

# Create list of words a child knows
words <- c("smile", "old", "chicken (animal)", "breakfast", "snow", "uh oh",
           "please", "bad", "bicycle", "moon")

# Create table
wg_categories <- wg2ws_items(words)

# Convert to WS score
ws_categories <- wg2ws_category_score(wg_categories, age = 20)

Get function for category/age combination

Description

Returns a model object to predict category score given category and age.

Usage

wg2ws_get_cat_function(the_category, age = TRUE, echo_only = FALSE)

Arguments

the_category

Which category to use, following Wordbank naming convention. Options: sounds, animals, vehicles, toys, food_drink, clothing, body_parts, household, furniture_rooms, outside, places, people, games_routines, action_words, descriptive_words, time_words, pronouns, question_words, locations, quantifiers, helping_verbs, connecting_words

age

T/F. If TRUE, return model that uses age as predictor.

echo_only

T/F. If FALSE, returns model as function; if TRUE echoes as human readable.

Details

This is mostly an internal function, but is exposed in case somebody needs it. Returns a lm() object that has had the embedded data stripped, given a category and whether to model age.

Value

Function or NULL

References

Day, T. K. M., Borovsky, A., Thal, D., & Elison, J. T. (2025). Modeling Longitudinal Trajectories of Word Production With the CDI. Developmental Science, 28(4), e70036. doi:10.1111/desc.70036

Examples

wg2ws_get_cat_function("time_words", age = TRUE)

List of items to category table

Description

Given a list of items, create a table of category scores

Usage

wg2ws_items(items, error_on_missing = TRUE, in_inside = "either")

Arguments

items

List of WG items present for individual.

error_on_missing

If TRUE, check whether all items are actual WG items. See helper function wg2ws_list_items().

in_inside

"In" and "inside" appear as two items on WG, but one ("inside/in") on WS. If "either," treat "inside/in" as endorsed if either appears. For "both", both must be endorsed. For "in" or "inside", treat "inside/in" as endorsed based solely on the presence of the indicated item.

Details

Requires a list that exactly matches items as labeled from Wordbank (check g_dict). Converts to a table of category scores, ready for use with wg2ws_category_score().

Value

A data frame with 22 rows indicating item totals for all WS categories. These values are not adjusted, and need to be adjusted with wg2ws_category_score().

Examples

# Create list of words a child knows
words <- c("smile", "old", "chicken (animal)", "breakfast", "snow", "uh oh",
           "please", "bad", "bicycle", "moon")
# Create table
categories <- wg2ws_items(words)

List instrument items

Description

List instrument items

Usage

wg2ws_list_items(instrument)

Arguments

instrument

"WG" or "WS"

Details

Simply list the items from each instrument for convenience.

Value

List of items

Examples

wg2ws_list_items("WG")
wg2ws_list_items("WS")

Summarize category table

Description

Summarize category table

Usage

wg2ws_summarize_cat(category_scores)

Arguments

category_scores

A 22x2 category result table

Details

Given a 22x2 category table, calculate total scores and lexical and syntax scores.

Value

A three column data frame, with total score, lexical, and syntactic scores

References

Day, T. K. M., & Elison, J. T. (2021). A broadened estimate of syntactic and lexical ability from the MB-CDI. Journal of Child Language, 49(3), 615-632. doi:10.1017/S0305000921000283

Examples


words <- c("smile", "old", "chicken (animal)", "breakfast", "snow", "uh oh",
           "please", "bad", "bicycle", "moon")

categories <- wg2ws_items(words)
scores <- wg2ws_summarize_cat(categories)

Calculate WS total score from WG score.

Description

Calculate WS total score from WG score.

Usage

wg2ws_total_age(WG, age = NA)

Arguments

WG

Words and Gestures total score.

age

Age in months (optional). A different, more accurate model is used if age is supplied.

Details

Given a single number (WG total score) and optionally age, calculate a WG score.

Value

Adjusted score, rounded to the nearest integer. Does not return values below 0 or greater than 680.

References

Day, T. K. M., Borovsky, A., Thal, D., & Elison, J. T. (2025). Modeling Longitudinal Trajectories of Word Production With the CDI. Developmental Science, 28(4), e70036. doi:10.1111/desc.70036

Examples


wg2ws_total_age(200)
wg2ws_total_age(200, age = 21)