Package 'EpiScopeK'

Title: Comprehensive Epidemiological Analysis Toolkit
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).
Authors: Khalid Ul Islam Rather [aut, cre]
Maintainer: Khalid Ul Islam Rather <[email protected]>
License: GPL-3
Version: 0.1.1
Built: 2026-07-07 17:16:25 UTC
Source: https://github.com/cran/EpiScopeK

Help Index

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")