| Type: | Package |
| Title: | Comprehensive Epidemiological Analysis Toolkit |
| Version: | 0.1.1 |
| Description: | Provides a unified framework for epidemiological data analysis and disease surveillance. The package supports descriptive epidemiology, incidence, prevalence and mortality estimation, age standardization, trend analysis, geographic summaries, disease risk prediction, and automated analytical workflows. Designed for researchers and public health professionals, it facilitates reproducible analyses of epidemiological datasets using established statistical and predictive modeling techniques. Methods are informed by standard epidemiological references including Rothman et al. (2008, ISBN:9780781755641) and Gordis (2014, ISBN:9781455737338). |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | dplyr, rlang |
| Suggests: | testthat |
| Depends: | R (≥ 4.1.0) |
| Config/roxygen2/version: | 8.0.0 |
| NeedsCompilation: | no |
| Packaged: | 2026-07-01 11:34:53 UTC; Dr. Imtiyaz |
| Author: | Khalid Ul Islam Rather [aut, cre] |
| Maintainer: | Khalid Ul Islam Rather <drkhalidulislam@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-07-07 08:40:02 UTC |
Epidemiological Example Dataset
Description
A simulated epidemiological dataset containing demographic and disease indicators.
Usage
epi_data
Format
A data frame with 10000 observations:
- country
Country
- year
Year
- sex
Sex
- age_group
Age Group
- incidence
Incidence Rate
- prevalence
Prevalence Rate
- mortality
Mortality Rate
Source
Simulated Data
Automated Epidemiological Pipeline
Description
Runs a complete epidemiological workflow.
Usage
epi_pipelineK(data)
Arguments
data |
Data frame. |
Value
List of results.
Examples
epi_pipelineK(epi_data)
Epidemiological Summary Statistics
Description
Computes summary statistics for all numeric variables.
Usage
epi_summaryK(data)
Arguments
data |
A data frame. |
Value
A data frame containing mean, standard deviation, median and number of missing values.
Examples
epi_summaryK(epi_data)
Geographic Summary of Epidemiological Data
Description
Aggregates an indicator by region.
Usage
geo_epiK(data, region, indicator)
Arguments
data |
Data frame. |
region |
Region variable. |
indicator |
Indicator variable. |
Value
Summary table.
Examples
geo_epiK(epi_data, "country", "incidence")
Calculate Incidence Rate
Description
Computes incidence rate per 100,000 population.
Usage
incidence_rateK(cases, population)
Arguments
cases |
Number of new cases. |
population |
Population at risk. |
Value
Numeric incidence rate.
Examples
incidence_rateK(150, 100000)
Calculate Mortality Rate
Description
Computes mortality rate per 100,000 population.
Usage
mortality_rateK(deaths, population)
Arguments
deaths |
Number of deaths. |
population |
Population size. |
Value
Numeric mortality rate.
Examples
mortality_rateK(50,100000)
Calculate Prevalence Rate
Description
Computes prevalence per 100,000 population.
Usage
prevalence_rateK(existing_cases, population)
Arguments
existing_cases |
Number of existing cases. |
population |
Total population. |
Value
Numeric prevalence rate.
Examples
prevalence_rateK(500,100000)
Disease Risk Prediction
Description
Fits a logistic regression model.
Usage
risk_predictK(formula, data)
Arguments
formula |
Model formula. |
data |
Data frame. |
Value
A fitted logistic regression model.
Examples
df <- data.frame(
disease = c(0,1,0,1,1,0),
age = c(25,40,35,60,55,30),
incidence = c(10,20,15,30,25,12)
)
risk_predictK(
disease ~ age + incidence,
df
)
Trend Analysis for Epidemiological Data
Description
Fits a linear trend model and visualizes temporal changes.
Usage
trend_epiK(data, year, outcome)
Arguments
data |
Data frame. |
year |
Name of year variable. |
outcome |
Name of outcome variable. |
Value
Linear model object.
Examples
trend_epiK(epi_data, "year", "incidence")