dmai_app(
launch.browser = TRUE
)
2026-11-05
\[ \newcommand{\vy}{\mathbf{y}} \newcommand{\vx}{\mathbf{x}} \newcommand{\vb}{\mathbf{b}} \newcommand{\ve}{\mathbf{e}} \newcommand{\vone}{\mathbf{1}} \newcommand{\vbeta}{\boldsymbol{\beta}} \newcommand{\vmu}{\boldsymbol{\mu}} \newcommand{\mX}{\mathbf{X}} \newcommand{\mZ}{\mathbf{Z}} \newcommand{\mV}{\mathbf{V}} \newcommand{\mI}{\mathbf{I}} \newcommand{\mJ}{\mathbf{J}} \newcommand{\mSigma}{\boldsymbol{\Sigma}} \newcommand{\E}{\mathrm{E}} \newcommand{\Var}{\mathrm{Var}} \newcommand{\Cov}{\mathrm{Cov}} \newcommand{\tr}{\operatorname{tr}} \newcommand{\vr}{\mathbf{r}} \newcommand{\vs}{\mathbf{s}} \newcommand{\vpi}{\boldsymbol{\pi}} \]
dmai_app() opens an application in the web browser that computes monetary aggregates without code: the data and its columns, one index with its weights and user costs, every method side by side, the user costs and the Divisia second moments. This vignette describes its pages and how it keeps its results reproducible.
dmai_app(
launch.browser = TRUE
)The application needs the suggested packages shiny, bslib and DT. magick with WebP support is optional: with it the plots are sent to the browser as WebP images, smaller than the same PNG.
| Page | What it does |
|---|---|
| Home | what the application does and the order of the pages |
| Data | the simulated example money_sim or a CSV file with one row per date; the date, quantity and rate columns (in the same order), the benchmark rate (a column, or the largest component rate) and whether the rates are in percent |
| Index | one method (Divisia, Divisia with simple growth, Fisher, simple sum, currency equivalent), the value at the first date and the logarithm of the growth; interpretation, plot, table, printed output and R code |
| Compare | the chosen methods side by side, as indexes or growth rates |
| User costs | the user cost and weight of every component over time |
| Second moments | the Divisia variance of the growth of the components |
| References | the articles behind the methods, as dmai_cite() prints them |
Every result page has the same tabs:
dmai_interpret(), with a button that copies it.The index page has a further tab, R code, with the code that reproduces the result outside the application.
The index page after the Divisia aggregate of money_sim with the bond yield rb as benchmark:
its plot, the aggregate against the simple sum:
the comparison of the methods:
and the weights of the components:
For the settings of the screenshots, the R code tab shows:
settings <-
list(
source = "example"
, method = "Barnett"
, logbase = "e"
, date = "Date"
, x = paste0("x", 1:8)
, r = paste0("r", 1:8)
, bench = "rb"
, percent = TRUE
, base = 100
)
cat(dmai:::.dmai_app_code(input = settings))library(dmai)
data(money_sim)
dat <- money_sim
fit <-
dmai(
.data = dat
, method = "Barnett"
, logbase = NULL
, date = "Date"
, x = c("x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8")
, r = c("r1", "r2", "r3", "r4", "r5", "r6", "r7", "r8")
, user_cost = NULL
, benchmark = "rb"
, percent = TRUE
, base = 100
, as_dt = FALSE
)
fit
plot(x = fit)
dmai_interpret(
x = fit
, top = 3
)The indexes are deterministic, so the code returns the same numbers as the application.
dmai_app_object() returns the application object without running it, for a server:
# app.R on a Shiny server
dmai::dmai_app_object()Barnett, W. A. (1980). Economic monetary aggregates: an application of index number and aggregation theory. Journal of Econometrics, 14(1), 11–48.