Package: epiR 2.0.75

Mark Stevenson

epiR: Tools for the Analysis of Epidemiological Data

Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.

Authors:Mark Stevenson [aut, cre], Evan Sergeant [aut], Cord Heuer [ctb], Telmo Nunes [ctb], Cord Heuer [ctb], Jonathon Marshall [ctb], Javier Sanchez [ctb], Ron Thornton [ctb], Jeno Reiczigel [ctb], Jim Robison-Cox [ctb], Paola Sebastiani [ctb], Peter Solymos [ctb], Kazuki Yoshida [ctb], Geoff Jones [ctb], Sarah Pirikahu [ctb], Simon Firestone [ctb], Ryan Kyle [ctb], Johann Popp [ctb], Mathew Jay [ctb], Allison Cheung [ctb], Nagendra Singanallur [ctb], Aniko Szabo [ctb], Ahmad Rabiee [ctb]

epiR_2.0.75.tar.gz
epiR_2.0.75.tar.gz(r-4.5-noble)epiR_2.0.75.tar.gz(r-4.4-noble)
epiR_2.0.75.tgz(r-4.4-emscripten)epiR_2.0.75.tgz(r-4.3-emscripten)
epiR.pdf |epiR.html✨
epiR/json (API)
NEWS

# Install epiR in R:
install.packages('epiR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • epi.SClip - Lip cancer in Scotland 1975 - 1980
  • epi.epidural - Rates of use of epidural anaesthesia in trials of caregiver support
  • epi.incin - Laryngeal and lung cancer cases in Lancashire 1974 - 1983

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

93 exports 9 stars 2.94 score 80 dependencies 11 dependents 199 mentions 11.5k downloads

Last updated 15 days agofrom:62b4633138fc9951b374f7c4e17eeb54c4a39523

Exports:epi.2by2epi.aboutepi.ascepi.betabusterepi.blcm.parasepi.bohningepi.cccepi.confepi.convgridepi.cpepi.cpresidsepi.descriptivesepi.dgammaepi.directadjepi.dmsepi.dslepi.edrepi.empbayesepi.herdtestepi.indirectadjepi.insthazepi.interactionepi.ivepi.kappaepi.ltdepi.mhepi.nomogramepi.occcepi.offsetepi.pooledepi.popsizeepi.prccepi.prevepi.psiepi.realriskepi.RtoBUGSepi.smdepi.smrepi.ssccepi.ssclus1estbepi.ssclus1estcepi.ssclus2estbepi.ssclus2estcepi.sscohortcepi.sscohorttepi.sscompbepi.sscompcepi.sscompsepi.ssdetectepi.ssdxsespepi.ssdxtestepi.ssequbepi.ssequcepi.ssninfbepi.ssninfcepi.sssimpleestbepi.sssimpleestcepi.ssstrataestbepi.ssstrataestcepi.sssupbepi.sssupcepi.ssxsectnepi.testsrsu.adjriskrsu.dxtestrsu.epinfrsu.pfree.eqursu.pfree.rsrsu.pstarrsu.seprsu.sep.censrsu.sep.passrsu.sep.rbrsu.sep.rb1rfrsu.sep.rb2rfrsu.sep.rb2strsu.sep.rbvarsersu.sep.rsrsu.sep.rs2strsu.sep.rsfreecalcrsu.sep.rsmultrsu.sep.rspoolrsu.sep.rsvarsersu.spp.rsrsu.sspfree.rsrsu.sssep.rb2st1rfrsu.sssep.rb2st2rfrsu.sssep.rbmrgrsu.sssep.rbsrgrsu.sssep.rsrsu.sssep.rs2strsu.sssep.rsfreecalcrsu.sssep.rspool

Dependencies:askpassbase64encBiasedUrnbslibcachemclassclassIntclicommonmarkcpp11crayoncrulcurldata.tableDBIdigeste1071evaluatefastmapflextablefontawesomefontBitstreamVerafontLiberationfontquiverfsgdtoolsgenericsgfontsgluehighrhtmltoolshttpcodehttpuvjquerylibjsonliteKernSmoothknitrlaterlatticelifecyclelubridatemagrittrMASSMatrixmemoisemimeofficeropensslpanderpromisesproxyR6raggrappdirsRcpprlangrmarkdowns2sasssfshinysourcetoolssurvivalsyssystemfontstextshapingtimechangetinytextriebeardunitsurltoolsuuidwithrwkxfunxml2xtableyamlzipzoo

Descriptive epidemiology using

Mark Stevenson

Rendered fromepiR_descriptive.Rmdusingknitr::rmarkdownon Jun 18 2024.

Last update: 2024-06-18
Started: 2020-03-13

Surveillance system assessment using

Mark Stevenson and Evan Sergeant

Rendered fromepiR_surveillance.Rmdusingknitr::rmarkdownon Jun 18 2024.

Last update: 2023-11-21
Started: 2020-12-04

Measures of association using

Mark Stevenson

Rendered fromepiR_measures_of_association.Rmdusingknitr::rmarkdownon Jun 18 2024.

Last update: 2024-01-10
Started: 2021-07-19

Sample size calculations using

Mark Stevenson

Rendered fromepiR_sample_size.Rmdusingknitr::rmarkdownon Jun 18 2024.

Last update: 2023-11-21
Started: 2021-01-12

Readme and manuals

Help Manual

Help pageTopics
Summary measures for count data presented in a 2 by 2 tableepi.2by2 print.epi.2by2 summary.epi.2by2
The library epiR: summary informationepi.about
Write matrix to an ASCII raster fileepi.asc
An R version of Wes Johnson and Chun-Lung Su's Betabusterepi.betabuster
Number of parameters to be inferred and number of informative priors required for a Bayesian latent class modelepi.blcm.paras
Bohning's test for overdispersion of Poisson dataepi.bohning
Concordance correlation coefficientepi.ccc
Confidence intervals for means, proportions, incidence, and standardised mortality ratiosepi.conf
Convert British National Grid georeferences to easting and northing coordinatesepi.convgrid
Extract unique covariate patterns from a data setepi.cp
Covariate pattern residuals from a logistic regression modelepi.cpresids
Descriptive statisticsepi.descriptives
Estimate the precision of a [structured] heterogeneity termepi.dgamma
Directly adjusted incidence rate estimatesepi.directadj
Decimal degrees and degrees, minutes and seconds conversionepi.dms
Mixed-effects meta-analysis of binary outcomes using the DerSimonian and Laird methodepi.dsl
Estimated dissemination ratioepi.edr
Empirical Bayes estimates of observed event countsepi.empbayes
Rates of use of epidural anaesthesia in trials of caregiver supportepi.epidural
Estimate the characteristics of diagnostic tests applied at the herd (group) levelepi.herdtest
Laryngeal and lung cancer cases in Lancashire 1974 - 1983epi.incin
Indirectly adjusted incidence risk estimatesepi.indirectadj
Event instantaneous hazard based on Kaplan-Meier survival estimatesepi.insthaz
Relative excess risk due to interaction in a case-control studyepi.interaction
Fixed-effects meta-analysis of binary outcomes using the inverse variance methodepi.iv
Kappa statisticepi.kappa
Lactation to date and standard lactation milk yieldsepi.ltd
Fixed-effects meta-analysis of binary outcomes using the Mantel-Haenszel methodepi.mh
Post-test probability of disease given sensitivity and specificity of a testepi.nomogram
Overall concordance correlation coefficient (OCCC)epi.occc print.epi.occc summary.epi.occc
Create offset vectorepi.offset
Estimate herd test characteristics when pooled sampling is usedepi.pooled
Estimate population size on the basis of capture-recapture samplingepi.popsize
Partial rank correlation coefficientsepi.prcc
Estimate true prevalence and the expected number of false positivesepi.prev
Proportional similarity indexepi.psi
An R version of the Winton Centre's RealRisk calculatorepi.realrisk
R to WinBUGS data conversionepi.RtoBUGS
Lip cancer in Scotland 1975 - 1980epi.SClip
Fixed-effects meta-analysis of continuous outcomes using the standardised mean difference methodepi.smd
Confidence intervals and tests of significance of the standardised mortality [morbidity] ratioepi.smr
Sample size, power or minimum detectable odds ratio for an unmatched or matched case-control studyepi.sscc
Sample size to estimate a binary outcome using one-stage cluster samplingepi.ssclus1estb
Sample size to estimate a continuous outcome using one-stage cluster samplingepi.ssclus1estc
Number of clusters to be sampled to estimate a binary outcome using two-stage cluster samplingepi.ssclus2estb
Number of clusters to be sampled to estimate a continuous outcome using two-stage cluster samplingepi.ssclus2estc
Sample size, power or minimum detectable incidence risk ratio for a cohort study using individual count dataepi.sscohortc
Sample size, power or minimum detectable incidence rate ratio for a cohort study using person or animal time dataepi.sscohortt
Sample size and power when comparing binary outcomesepi.sscompb
Sample size and power when comparing continuous outcomesepi.sscompc
Sample size and power when comparing time to eventepi.sscomps
Sample size to detect an eventepi.ssdetect
Sample size to estimate the sensitivity or specificity of a diagnostic testepi.ssdxsesp
Sample size to validate a diagnostic test in the absence of a gold standardepi.ssdxtest
Sample size for a parallel equivalence or equality trial, binary outcomeepi.ssequb
Sample size for a parallel equivalence or equality trial, continuous outcomeepi.ssequc
Sample size for a non-inferiority trial, binary outcomeepi.ssninfb
Sample size for a non-inferiority trial, continuous outcomeepi.ssninfc
Sample size to estimate a binary outcome using simple random samplingepi.sssimpleestb
Sample size to estimate a continuous outcome using simple random samplingepi.sssimpleestc
Sample size to estimate a binary outcome using stratified random samplingepi.ssstrataestb
Sample size to estimate a continuous outcome using a stratified random sampling designepi.ssstrataestc
Sample size for a parallel superiority trial, binary outcomeepi.sssupb
Sample size for a parallel superiority trial, continuous outcomeepi.sssupc
Sample size, power or minimum detectable prevalence ratio or odds ratio for a cross-sectional studyepi.ssxsectn
Sensitivity, specificity and predictive value of a diagnostic testepi.tests print.epi.tests summary.epi.tests
Adjusted risk valuesrsu.adjrisk
Sensitivity and specificity of diagnostic tests interpreted in series or parallelrsu.dxtest
Effective probability of diseasersu.epinf
Equilibrium probability of disease freedom assuming representative or risk based samplingrsu.pfree.equ
Calculate the probability of freedom for given population sensitivity and probability of introductionrsu.pfree.rs
Design prevalence back calculationrsu.pstar
Probability that the prevalence of disease in a population is less than or equal to a specified design prevalencersu.sep
Surveillance system sensitivity assuming data from a population censusrsu.sep.cens
Surveillance system sensitivity assuming passive surveillance and representative sampling within clustersrsu.sep.pass
Surveillance system sensitivity assuming risk-based sampling and varying unit sensitivityrsu.sep.rb
Surveillance system sensitivity assuming risk-based sampling on one risk factorrsu.sep.rb1rf
Surveillance system sensitivity assuming risk-based sampling on two risk factorsrsu.sep.rb2rf
Surveillance system sensitivity assuming risk based, two-stage samplingrsu.sep.rb2st
Surveillance system sensitivity assuming risk based sampling and varying unit sensitivityrsu.sep.rbvarse
Surveillance system sensitivity assuming representative samplingrsu.sep.rs
Surveillance system sensitivity assuming representative two-stage samplingrsu.sep.rs2st
Surveillance system sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.rsu.sep.rsfreecalc
Surveillance system sensitivity by combining multiple surveillance componentsrsu.sep.rsmult
Surveillance system sensitivity assuming representative sampling, imperfect pooled sensitivity and perfect pooled specificityrsu.sep.rspool
Surveillance system sensitivity assuming representative sampling and varying unit sensitivityrsu.sep.rsvarse
Surveillance system specificity assuming representative samplingrsu.spp.rs
Sample size to achieve a desired probability of disease freedom assuming representative samplingrsu.sspfree.rs
Sample size to achieve a desired surveillance system sensitivity assuming risk-based 2-stage sampling on one risk factor at the cluster levelrsu.sssep.rb2st1rf
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 bothrsu.sssep.rb2st2rf
Sample size to achieve a desired surveillance system sensitivity assuming risk-based sampling and multiple sensitivity values within risk groupsrsu.sssep.rbmrg
Sample size to achieve a desired surveillance system sensitivity assuming risk-based sampling and a single sensitivity value for each risk grouprsu.sssep.rbsrg
Sample size to achieve a desired surveillance system sensitivity assuming representative samplingrsu.sssep.rs
Sample size to achieve a desired surveillance system sensitivity assuming two-stage samplingrsu.sssep.rs2st
Sample size to achieve a desired surveillance system sensitivity to detect disease at a specified design prevalence assuming representative sampling, imperfect unit sensitivity and specificityrsu.sssep.rsfreecalc
Sample size to achieve a desired surveillance system sensitivity using pooled samples assuming representative samplingrsu.sssep.rspool