Related R packages for descriptive data analyses

[dirkschumacher]

Scope: pratical packages on CRAN that can be used in an outbreak setting. No pure research methods.

• epiR Tools for the analysis of epidemiological 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, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
• EpiStatsProvides set of functions aimed at epidemiologists. They include commands for measures of association and impact for case control studies and cohort studies. They may be particularly useful for outbreak investigations and include univariate and stratified analyses. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands.
• RSurveillance A range of functions for the design and analysis of disease surveillance activities. These functions were originally developed for animal health surveillance activities but can be equally applied to aquatic animal, wildlife, plant and human health surveillance activities. Utilities are included for sample size calculation and analysis of representative surveys for disease freedom, risk-based studies for disease freedom and for prevalence estimation.
• pubh A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand lattice plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.
• Epi Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data, in particular representation, manipulation and simulation of multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Also contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.
• scanstatistics Detection of anomalous space-time clusters using the scan statistics methodology. Focuses on prospective surveillance of data streams, scanning for clusters with ongoing anomalies. Hypothesis testing is made possible by Monte Carlo simulation.
• surveillanceStatistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Höhle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. 'hhh4' estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. 'twinSIR' models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Höhle (2009) <doi:10.1002/bimj.200900050>. 'twinstim' estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
• epitrix A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymise data, and fitting discretised Gamma distributions.
• epitools Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
• epitab Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: 'HTML', 'LaTeX', and 'Excel'.
• epinet A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
• epiflows Provides functions and classes designed to handle and visualise epidemiological flows between locations. Also contains a statistical method for predicting disease spread from flow data initially described in Dorigatti et al. (2017) <doi:10.2807/1560-7917.ES.2017.22.28.30572>. This package is part of the RECON (http://www.repidemicsconsortium.org/) toolkit for outbreak analysis.
• EpiEstim This package provides tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence.
• epiDisplay Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at 'http://medipe.psu.ac.th/epicalc'.
• EpiCurve Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.
• EpiContactTrace Routines for epidemiological contact tracing and visualisation of network of contacts.
• epicontacts A collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.
• epibasix This package contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. Source code is commented throughout to facilitate modification. The target audience includes advanced undergraduate and graduate students in epidemiology or biostatistics courses, and clinical researchers.
• incidence Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON (http://www.repidemicsconsortium.org/) toolkit for outbreak analysis.

[aspina7]

Crazy, didnt realise there were this many. The epistats package is the one recently release by epiconcept, which tries to mimic stata, but think Dan's function is nicer than theirs (see other comment).

So far every basic epi package I have encountered has at least one glitch or one error in calculating something... (or is no longer actively maintained)

[zkamvar]

This is a hell of a list. I wonder what the code health looks like (age, tests, CI, dependencies).

[zkamvar]

library("packagemetrics")
library("dplyr")
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library("knitr")

pkgs <- "epitools
epiR
EpiStats
RSurveillance
pubh
Epi
scanstatistics
surveillance
epitrix
epitools
epitab
epinet
epiflows
EpiEstim
epiDisplay
EpiCurve
EpiContactTrace
epicontacts
epibasix
incidence"
pkg_vec <- strsplit(pkgs, "\n")[[1]]
pm <- packagemetrics::package_list_metrics(pkg_vec)

pm <- arrange(pm, 
              desc(has_tests),
              desc(ci),
              depends_count,
              desc(reverse_count),
              desc(published)
              )
ft <- packagemetrics::metrics_table(pm)
ft

package	published	dl\_last\_month	stars	tidyverse\_happy	has\_tests	vignette	last\_commit	last\_issue\_closed	contributors	depends\_count	reverse\_count
epitrix	2018-08-28	339	4				0.9	0.9	3	1	1
incidence	2018-08-24	562	13					0.5	6		1
epicontacts	2017-11-21	298	5					1.3	0		1
epiflows	2018-08-14	215	7					2.6	2	1	0
scanstatistics	2018-01-24	232	26					1.9	1	1	0
surveillance	2018-07-24	1058								8	1
EpiContactTrace	2017-10-19	337	2						10	1	0
epitab	2018-07-04	280	1						0		0
pubh	2018-08-30	237	0						0	5	0
epitools	2017-10-26	7691								1	8
epitools	2017-10-26	7691								1	8
epibasix	2012-11-15	660								1	0
Epi	2018-08-23	6555								2	9
epiR	2018-08-22	4054								2	7
EpiStats	2018-10-08	624								2	0
epiDisplay	2018-05-10	1904								5	1
EpiCurve	2018-04-24	369								5	0
EpiEstim	2013-03-08	341									1
epinet	2018-02-13	324									0
RSurveillance	2016-10-04	212									0

^{Created on 2018-10-30 by the reprex package (v0.2.1)}

[zkamvar]

Oh, hell. Here's the image:

[dirkschumacher]

Nice

[thibautjombart]

Awesome. And super useful. @zkamvar Could be nice to turn this into something we can put online actually - on repidemicsconsortium/projects :)