Package 'MVPapp'

Description

A virtual pediatric population (birth to 20 years) simulated from the US Centers for Disease Control and Prevention (CDC) growth charts. Used by the app to sample age, weight and height when the "CDC" database is selected for variability simulations.

Usage

cdc.expand

Format

A data frame with 97,177 rows and 6 columns:

SEX

Sex: 0 = male, 1 = female.

AGEMO

Age (months).

AGEYR

Age (years).

WT

Body weight (kg).

HT

Length/stature (cm).

BMI

Body mass index (kg/m$^2$).

Details

Body weight and stature were simulated from the published LMS parameters (Box-Cox power, median and coefficient of variation) of the weight-for-age and stature-for-age charts for each sex and month of age, by drawing standard normal Z-scores and applying $X = M (1 + L S Z)^{1/L}$. Rows with implausible BMI (outside 10 to 50 kg/m$^2$) were removed. See data-raw/databases.R in the GitHub repository for the code.

Source

CDC growth charts, percentile data files with LMS values: https://www.cdc.gov/growthcharts/percentile_data_files.htm.

See Also

nhanes.filtered, who.expand

Description

Sex, age, body weight and height of participants in the US National Health and Nutrition Examination Survey (NHANES), cycles 1999-2000 to 2021-2023, aged 2 years and older. Used by the app to sample age, weight and height when the "NHANES" database is selected for variability simulations.

Usage

nhanes.filtered

Format

A data frame with 101,400 rows and 5 columns:

SEX

Sex: 0 = male, 1 = female.

AGEMO

Age (months), derived from AGEYR.

AGEYR

Age (years).

WT

Body weight (kg).

HT

Standing height (cm).

Details

Demographics and body measures files were merged by participant and only complete records were kept. Age is recorded in whole years, so AGEMO is AGEYR * 12. See data-raw/databases.R in the GitHub repository for the code.

Source

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics, National Health and Nutrition Examination Survey: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.

See Also

cdc.expand, who.expand

Description

run_mvp() launches the MVP app. mvp_app() returns the app as a Shiny app object without running it, e.g. for deployment on Posit Connect or shinyapps.io (an app.R containing MVPapp::mvp_app() is sufficient).

The app is evaluated in its own environment (whose parent gives access to MVPapp and its key dependencies), so neither the global environment nor the search path is modified. Global options changed by the app are set when it starts and restored when it stops.

Usage

mvp_app(
  appDir = system.file("shiny", package = "MVPapp"),
  insert_watermark = TRUE,
  max_dataset_size = 200,
  authentication_code = NA_character_,
  internal_version = TRUE,
  pw_models_path = NA_character_,
  llm_choices = c("Claude", "Gemini", "OpenAI", "OpenRouter", "OpenAI-Compatible",
    "DeepSeek", "Azure OpenAI", "AWS Bedrock"),
  api_upload = NA_character_,
  api_chat = NA_character_,
  user_id = "MVP_user",
  reuse_context = FALSE,
  model_gemini = "gemini-3.8-flash",
  model_openai = "gpt-6.1-sol",
  model_anthropic = "claude-sonnet-5-5",
  model_openrouter = "arcee-ai/trinity-large-preview:free",
  model_openai_compatible = "gpt-5.2",
  model_deepseek = "deepseek-flash",
  model_apollo = "claude_4_6_sonnet",
  model_azure = "gpt-5.2",
  model_aws = "anthropic.claude-sonnet-5",
  temperature = 1,
  llm_seed = 42,
  model_lang = c("mrgsolve", "nonmem", "rxode2"),
  prompts_path = system.file("shiny/prompts.R", package = "MVPapp"),
  show_debugging_msg = FALSE,
  brand_color = NULL,
  brand_text_color = NULL,
  brand_logo = NULL,
  brand_favicon = NULL,
  page_title = NULL,
  ...
)

run_mvp(
  appDir = system.file("shiny", package = "MVPapp"),
  insert_watermark = TRUE,
  max_dataset_size = 200,
  authentication_code = NA_character_,
  internal_version = TRUE,
  pw_models_path = NA_character_,
  llm_choices = c("Claude", "Gemini", "OpenAI", "OpenRouter", "OpenAI-Compatible",
    "DeepSeek", "Azure OpenAI", "AWS Bedrock"),
  api_upload = NA_character_,
  api_chat = NA_character_,
  user_id = "MVP_user",
  reuse_context = FALSE,
  model_gemini = "gemini-3.8-flash",
  model_openai = "gpt-6.1-sol",
  model_anthropic = "claude-sonnet-5-5",
  model_openrouter = "arcee-ai/trinity-large-preview:free",
  model_openai_compatible = "gpt-5.2",
  model_deepseek = "deepseek-flash",
  model_apollo = "claude_4_6_sonnet",
  model_azure = "gpt-5.2",
  model_aws = "anthropic.claude-sonnet-5",
  temperature = 1,
  llm_seed = 42,
  model_lang = c("mrgsolve", "nonmem", "rxode2"),
  prompts_path = system.file("shiny/prompts.R", package = "MVPapp"),
  show_debugging_msg = FALSE,
  brand_color = NULL,
  brand_text_color = NULL,
  brand_logo = NULL,
  brand_favicon = NULL,
  page_title = NULL,
  ...
)

Arguments

Details

Objects kept for debugging. MVPapp does not write to the global environment. Objects useful for debugging are kept in an internal environment, MVPapp:::.mvp_state, and remain available in the R session after the app is closed. List them with ls(MVPapp:::.mvp_state):

• last_ev_df$model_1, last_ev_df$model_2: the dosing event tables used in each model's most recent simulation (also used for the reproducible bundle download).

• deps_env: an internal cache used to run the app; not useful for debugging.

• With show_debugging_msg = TRUE, AI translation responses: llm_result (initial translation), llm_refine (latest retry that still failed), llm_success (Dify's reply when told the code compiled), branch_model / branch_refine (raw answers before cleaning) and ellmer_chat_response / dify_chat_response (raw chat responses).

Branding. For a company deployment, the title, colour, logo and favicon can be set when launching, without editing any package files. Each is optional; with all of them left at NULL the app looks as it always did.

run_mvp(brand_color   = "#F36633",
        brand_logo    = "path/to/company_logo.png",
        brand_favicon = "path/to/favicon.png",
        page_title    = "Company Model Visualization Platform")

Image files are read from where they are and served by the app, so they do not need to be inside the package (a web URL also works). A logo with a transparent background and a height of about 30 pixels or more works best. brand_color replaces the navy in the navbar, the active tab, the headers and borders of the panels, the data table headers and the loading spinners. The same arguments work with mvp_app() for hosted deployments.

Hosting and security. MVPapp evaluates R and C++ model code supplied in the browser, so anyone who can open the app can run code on the server under the server account. authentication_code and the model passwords are a convenience to limit access to the interface and to hide models, not a security boundary: host the app only for trusted users, in a sandboxed account or container. The same applies to AI translation: its output is compiled automatically, and compiling mrgsolve code can itself run commands, so do not translate documents from untrusted sources on a shared server.

Without installing MVPapp (e.g. where package installation is restricted), the development version can also be launched from GitHub, provided its dependencies are installed: shiny::runGitHub("MVPapp", "stevechoy", subdir = "inst/shiny").

Value

run_mvp() is called for its side effect of launching the app and returns the value of shiny::runApp() invisibly once the app is closed. mvp_app() returns a Shiny app object (class shiny.appobj).

See Also

https://stevechoy.github.io/MVPapp/articles/supply-passwords.html https://stevechoy.github.io/MVPapp/articles/automatic-translation.html chat for LLM chat interface.

Examples

if (interactive()) {
  run_mvp(insert_watermark = FALSE) # remove watermarks

  # Other examples:
  # run_mvp(launch.browser = FALSE) # do not open a browser (argument passed to runApp)
  # run_mvp(authentication_code = "some_password") # Password-lock the site,
  #                                                # could be useful in deployment
  # run_mvp(pw_models_path = "path/to/your/private/models.R") # see
  #   system.file("shiny/passworded_models_example.R", package = "MVPapp")
  # run_mvp(llm_choices = "Claude", model_anthropic = "claude-sonnet-5-5",
  #         temperature = 0) # Uses Claude only with reproducible results
}

# App object, e.g. as the only line of an app.R file for deployment:
app <- mvp_app(insert_watermark = FALSE)

Description

A virtual pediatric population (birth to 10 years) simulated from the World Health Organization (WHO) Child Growth Standards and Growth Reference. Used by the app to sample age, weight and height when the "WHO" database is selected for variability simulations.

Usage

who.expand

Format

A data frame with 77,744 rows and 6 columns:

SEX

Sex: 0 = male, 1 = female.

AGEMO

Age (months).

AGEYR

Age (years).

WT

Body weight (kg).

HT

Length/height (cm).

BMI

Body mass index (kg/m$^2$).

Details

Body weight and height were simulated from the published LMS parameters (Box-Cox power, median and coefficient of variation) for each sex and month of age, by drawing standard normal Z-scores and applying $X = M (1 + L S Z)^{1/L}$. Sources:

• Weight-for-age, 0 to 10 years.

• Length/height-for-age, 0 to 5 years (Child Growth Standards) and 5 to 10 years (Growth Reference 5-19 years).

Rows with implausible BMI (outside 10 to 50 kg/m$^2$) were removed. See data-raw/databases.R in the GitHub repository for the code.

Source

WHO Child Growth Standards: https://www.who.int/toolkits/child-growth-standards/standards/weight-for-age, https://www.who.int/toolkits/child-growth-standards/standards/length-height-for-age. WHO Growth Reference 5-19 years: https://www.who.int/toolkits/growth-reference-data-for-5to19-years/indicators/height-for-age.

See Also

cdc.expand, nhanes.filtered