In asshah4/card: Cardiovascular Applications in Research Data

knitr::opts_chunk$set(
  collapse = TRUE,
  cache = TRUE,
  comment = "#>",
  fig.path = "man/figures/",
  out.width = "100%"
)

card

Cardiovascular Applications for Research Data

The inspiration for {card} was to create useful functions and analytical approaches in computational neurocardiology, with a on electrocardiography and epidemiology.

The areas of focus of this package are the following:

• handling of recurrent and circadian events
• epidemiological focus on effective model building

Installation

You can install the released version of {card} from CRAN with:

install.packages("card")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("shah-in-boots/card")

Usage

The intent for card is to assist in the analysis of cardiovascular and epidemiological data, with a focus on cardiac-specific datasets and data types.

library(card)

Datasets

• Cardiovascular disease outcomes with recurrent events
• Vectorcardiography data in a high-risk cohort
• Continuous electrocardiography recordings in a clinical cohort with quantitative stress data

# ECG data
data("twins")
head(twins)

# Outcomes data
data("stress")
head(stress)

# VCG data
data("geh")
head(geh)

Cosinor Analysis

This modeling algorithm requires only base R, which allows internal flexibility for modeling heuristics and improved efficiency. For the user, it also allows standard modeling tools/approaches, and a flexible user interface that accounts for individual/population analysis and single/multiple component analysis.

There are other cosinor models available in R, however they do not allow for simple multiple-component or individual/population analysis in a method that is tidy (for both tidyverse and tidymodels approaches).

m <- cosinor(rDYX ~ hour, twins, tau = c(24, 12))
summary(m)
ggcosinor(m)

head(augment(m))

asshah4/card documentation built on Jan. 29, 2025, 12:02 a.m.