c(
person("Rafael H. M.", "Pereira", , "rafa.pereira.br@gmail.com", role = "aut",
comment = c(ORCID = "0000-0003-2125-7465")),
person("Daniel", "Herszenhut", , "dhersz@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-8066-1105")),
person("Ipea - Institute for Applied Economic Research", role = c("cph", "fnd"))
)
A set of fast and convenient functions to calculate multiple
transport accessibility measures. Given a pre-computed travel cost
matrix and a land use dataset (containing the location of jobs,
healthcare and population, for example), the package allows one to
calculate active and passive accessibility levels using multiple
accessibility measures, such as: cumulative opportunities (using
either travel cost cutoffs or intervals), minimum travel cost to
closest N number of activities, gravity-based (with different decay
functions) and different floating catchment area methods.
checkmate, data.table, Rdpack (>= 0.7), utils
covr, ggplot2, knitr, rmarkdown, sf, stats, testthat
Rafael H. M. Pereira [aut] (<https://orcid.org/0000-0003-2125-7465>),
Daniel Herszenhut [aut, cre] (<https://orcid.org/0000-0001-8066-1105>),
Ipea - Institute for Applied Economic Research [cph, fnd]

Improving the Accessibility of 'rmarkdown' Documents
c(person(given = "Rich",
family = "Leyshon",
role = c("aut", "cre"),
email = "richard.leyshon@ons.gov.uk"),
person(given = "Crown Copyright 2021", role = "cph")
)
Provides a simple method to improve the accessibility of
'rmarkdown' documents. The package provides functions for creating or
modifying 'rmarkdown' documents, resolving known errors and alerts that
result in accessibility issues for screen reader users.
Rich Leyshon [aut, cre],
Crown Copyright 2021 [cph]

Yu Jiang, Steve Simon, Matthew S. Mayo , Rama Raghavan, Byron J. Gajewski
Subject recruitment for medical research is challenging. Slow patient accrual leads to delay in research. Accrual monitoring during the process of recruitment is critical. Researchers need reliable tools to manage the accrual rate. We developed a Bayesian method that integrates researcher's experience on previous trials and data from the current study, providing reliable prediction on accrual rate for clinical studies. In this R package, we present functions for Bayesian accrual prediction which can be easily used by statisticians and clinical researchers.