The library epiR: summary information
Kappa statistic
R to WinBUGS data conversion
Sample size to estimate a binary outcome using one-stage cluster sampling
Fixed-effects meta-analysis of binary outcomes using the inverse variance method
Estimate the precision of a [structured] heterogeneity term
Decimal degrees and degrees, minutes and seconds conversion
Partial rank correlation coefficients
Estimate population size on the basis of capture-recapture sampling
Convert British National Grid georeferences to easting and northing coordinates
Sample size to estimate a continuous outcome using one-stage cluster sampling
Laryngeal and lung cancer cases in Lancashire 1974 - 1983
Estimated dissemination ratio
Directly adjusted incidence rate estimates
Empirical Bayes estimates of observed event counts
Covariate pattern residuals from a logistic regression model
Extract unique covariate patterns from a data set
Rates of use of epidural anaesthesia in trials of caregiver support
Indirectly adjusted incidence risk estimates
Lactation to date and standard lactation milk yields
Event instantaneous hazard based on Kaplan-Meier survival estimates
Sample size to estimate the sensitivity or specificity of a diagnostic test
Descriptive statistics
Mixed-effects meta-analysis of binary outcomes using the DerSimonian and Laird method
Proportional similarity index
Sample size to validate a diagnostic test in the absence of a gold standard
Sample size, power or minimum detectable incidence risk ratio for a cohort study using individual count data
Estimate true prevalence and the expected number of false positives
Fixed-effects meta-analysis of binary outcomes using the Mantel-Haenszel method
An R version of the Winton Centre's RealRisk calculator
Fixed-effects meta-analysis of continuous outcomes using the standardised mean difference method
Relative excess risk due to interaction in a case-control study
Sample size and power when comparing binary outcomes
Sample size for a non-inferiority trial, binary outcome
Estimate the characteristics of diagnostic tests applied at the herd (group) level
Sample size, power or minimum detectable incidence rate ratio for a cohort study using person or animal time data
Sample size to estimate a continuous outcome using a stratified random sampling design
Sample size and power when comparing continuous outcomes
Sample size to estimate a binary outcome using stratified random sampling
Sample size for a non-inferiority trial, continuous outcome
Calculate the probability of freedom for given population sensitivity and probability of introduction
Overall concordance correlation coefficient (OCCC)
Adjusted risk values
Post-test probability of disease given sensitivity and specificity of a test
Design prevalence back calculation
Create offset vector
Surveillance system sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.
Sensitivity and specificity of diagnostic tests interpreted in series or parallel
Effective probability of disease
Estimate herd test characteristics when pooled sampling is used
Surveillance system sensitivity by combining multiple surveillance components
Sample size to achieve a desired surveillance system sensitivity assuming representative sampling
Surveillance system sensitivity assuming representative sampling, imperfect pooled sensitivity and perfect pooled specificity
Number of clusters to be sampled to estimate a binary outcome using two-stage cluster sampling
Sample size to achieve a desired surveillance system sensitivity to detect disease at a specified design prevalence assuming representative sampling, imperfect unit sensitivity and specificity
Surveillance system sensitivity assuming representative sampling and varying unit sensitivity
Sample size to achieve a desired surveillance system sensitivity using pooled samples assuming representative sampling
Sample size to estimate a continuous outcome using simple random sampling
Confidence intervals and tests of significance of the standardised mortality [morbidity] ratio
Sample size and power when comparing time to event
Sample size for a parallel equivalence or equality trial, binary outcome
Sample size, power or minimum detectable odds ratio for an unmatched or matched case-control study
Sample size to estimate a binary outcome using simple random sampling
Number of clusters to be sampled to estimate a continuous outcome using two-stage cluster sampling
Sample size to achieve a desired surveillance system sensitivity assuming two-stage sampling
Sample size for a parallel equivalence or equality trial, continuous outcome
Sample size to detect an event
Probability that the prevalence of disease in a population is less than or equal to a specified design prevalence
Equilibrium probability of disease freedom assuming representative or risk based sampling
Surveillance system sensitivity assuming data from a population census
Surveillance system sensitivity assuming passive surveillance and representative sampling within clusters
Sample size to achieve a desired surveillance system sensitivity assuming risk-based 2-stage sampling on one risk factor at the cluster level
Sample size, power or minimum detectable prevalence ratio or odds ratio for a cross-sectional study
Surveillance system sensitivity assuming risk-based sampling and varying unit sensitivity
Sample size for a parallel superiority trial, continuous outcome
Surveillance system sensitivity assuming risk based, two-stage sampling
Sensitivity, specificity and predictive value of a diagnostic test
Sample size for a parallel superiority trial, binary outcome
Surveillance system sensitivity assuming risk based sampling and varying unit sensitivity
Sample size to achieve a desired surveillance system sensitivity assuming risk-based 2-stage sampling on two risk factors at either the cluster level, unit level, or both
Surveillance system sensitivity assuming risk-based sampling on one risk factor
Surveillance system sensitivity assuming representative sampling
Surveillance system sensitivity assuming risk-based sampling on two risk factors
Sample size to achieve a desired probability of disease freedom assuming representative sampling
Surveillance system specificity assuming representative sampling
Sample size to achieve a desired surveillance system sensitivity assuming risk-based sampling and multiple sensitivity values within risk groups
Sample size to achieve a desired surveillance system sensitivity assuming risk-based sampling and a single sensitivity value for each risk group
Surveillance system sensitivity assuming representative two-stage sampling
Write matrix to an ASCII raster file
Concordance correlation coefficient
Number of parameters to be inferred and number of informative priors required for a Bayesian latent class model
An R version of Wes Johnson and Chun-Lung Su's Betabuster
Summary measures for count data presented in a 2 by 2 table
Lip cancer in Scotland 1975 - 1980
Bohning's test for overdispersion of Poisson data
Confidence intervals for means, proportions, incidence, and standardised mortality ratios