methods, dplyr, curl, jsonlite, httr, stringr, rlang, vroom,
bib2df, tibble, progress, lubridate, purrr, data.table

Macroeconomic Expectations Data in R using the Central Bank of
Brazil API
person(given = "Fernando",
family = "da Silva",
role = c("aut", "cre"),
email = "fernando@fortietwo.com",
comment = c(ORCID = "0000-0002-7491-6930"))
Provides quick and easy access to market expectations data to the main macroeconomic indicators in the Focus report, made available by the Central Bank of Brazil through the Expectations System data API <https://dadosabertos.bcb.gov.br/>. This package offers an R interface to the API and other advantages, such as using a caching system and the option to use all the machine's cores (parallel computing).
dplyr, furrr, future, httr, jsonlite, memoise, purrr,
lubridate
testthat (>= 3.0.0), knitr, rmarkdown, utf8
Fernando da Silva [aut, cre] (<https://orcid.org/0000-0002-7491-6930>)

Analysis of Data with Measurement Error Using a Validation
Subsample
c(person(c("Walter", "K"), "Kremers",
email="kremers.walter@mayo.edu",
role=c("aut", "cre"),
comment = c(ORCID = "0000-0001-5714-3473") ))
Analyze data with measurement error when there is a validation subsample randomly selected from the full sample.  The method assumes surrogate variables measured with error are available for the full sample, and reference variables measured with little or no error are available for this randomly chosen subsample of the full sample.  Measurement errors may be differential or non differential, in any or all predictors (simultaneously) as well as outcome.  The "augmented" estimates derived are based upon the multivariate correlation between regression model parameter estimates for the reference variables and for the surrogate variables in the validation subset.  Because the validation subsample is chosen at random whatever biases are imposed by measurement error, non-differential or differential, are reflected in this correlation and can be used to derive estimates for the reference variables using data from the whole sample.  This work derives from Chen Y-H, Chen H. (2000) <doi:10.1111/1467-9868.00243>, Chen Y-H. (2002) <doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015) <doi:10.1016/j.jmva.2015.05.017> and Tong Y, Huang J et al. (2020) <doi:10.1093/jamia/ocz180>.
Mayo Foundation for Medical Education and Research
Walter K Kremers [aut, cre] (<https://orcid.org/0000-0001-5714-3473>)