Showing 23 of total 23 results (show query)
covid19datahub
COVID19:COVID-19 Data Hub
Unified datasets for a better understanding of COVID-19.
Maintained by Emanuele Guidotti. Last updated 27 days ago.
2019-ncovcoronaviruscovid-19covid-datacovid19-data
65.8 match 252 stars 11.08 score 265 scripts
mponce0
covid19.analytics:Load and Analyze Live Data from the COVID-19 Pandemic
Load and analyze updated time series worldwide data of reported cases for the Novel Coronavirus Disease (COVID-19) from different sources, including the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) data repository <https://github.com/CSSEGISandData/COVID-19>, "Our World in Data" <https://github.com/owid/> among several others. The datasets reporting the COVID-19 cases are available in two main modalities, as a time series sequences and aggregated data for the last day with greater spatial resolution. Several analysis, visualization and modelling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread.
Maintained by Marcelo Ponce. Last updated 3 years ago.
covid19covid19-datancov2019sars-cov-2
84.7 match 33 stars 5.52 score 20 scripts
epiforecasts
covid19.nhs.data:Covid 19 England Hospital Admissions
Facilitates access to data on English Covid-19 hospital admissions aggregated to a range of spatial scales.
Maintained by Sophie Meakin. Last updated 4 years ago.
covid19hospitalisationslocal-authoritynhsopenscienceuk
44.3 match 8 stars 4.61 score 17 scripts
bahlolab
UKB.COVID19:UK Biobank COVID-19 Data Processing and Risk Factor Association Tests
Process UK Biobank COVID-19 test result data for susceptibility, severity and mortality analyses, perform potential non-genetic COVID-19 risk factor and co-morbidity association tests. Wang et al. (2021) <doi:10.5281/zenodo.5174381>.
Maintained by Longfei Wang. Last updated 7 months ago.
42.3 match 1 stars 4.00 score 4 scripts
qingyuanzhao
bets.covid19:The BETS Model for Early Epidemic Data
Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.
Maintained by Qingyuan Zhao. Last updated 5 years ago.
37.5 match 27 stars 4.43 score 2 scripts
ramikrispin
coronavirus:The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset
Provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus <https://systems.jhu.edu/research/public-health/ncov/>.
Maintained by Rami Krispin. Last updated 2 years ago.
covid-19covid19covid19-datadataset
18.5 match 499 stars 8.25 score 716 scripts
gabriel-assuncao
COVIDIBGE:Downloading, Reading and Analyzing PNAD COVID19 Microdata
Provides tools for downloading, reading and analyzing the COVID19 National Household Sample Survey - PNAD COVID19, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
Maintained by Gabriel Assuncao. Last updated 1 years ago.
14.2 match 5 stars 3.88 score 2 scripts 1 dependentsramikrispin
covid19sf:The Covid19 San Francisco Dataset
Provides a verity of summary tables of the Covid19 cases in San Francisco. Data source: San Francisco, Department of Public Health - Population Health Division <https://datasf.org/opendata/>.
Maintained by Rami Krispin. Last updated 2 years ago.
6.8 match 12 stars 5.16 score 12 scripts
refunders
refund:Regression with Functional Data
Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.
Maintained by Julia Wrobel. Last updated 6 months ago.
3.4 match 41 stars 10.25 score 472 scripts 16 dependents
rodrigozepeda
covidmx:Descarga y analiza datos de COVID-19 en México
Herramientas para el análisis de datos de COVID-19 en México. Descarga y analiza los datos para COVID-19 de la Direccion General de Epidemiología de México (DGE) <https://www.gob.mx/salud/documentos/datos-abiertos-152127>, la Red de Infecciones Respiratorias Agudas Graves (Red IRAG) <https://www.gits.igg.unam.mx/red-irag-dashboard/reviewHome> y la Iniciativa Global para compartir todos los datos de influenza (GISAID) <https://gisaid.org/>. English: Downloads and analyzes data of COVID-19 from the Mexican General Directorate of Epidemiology (DGE), the Network of Severe Acute Respiratory Infections (IRAG network),and the Global Initiative on Sharing All Influenza Data GISAID.
Maintained by Rodrigo Zepeda-Tello. Last updated 8 months ago.
covid-19covid-datacovid19covidmxdatos-abiertosdatos-gob-mxmexicomexico-datos
11.0 match 1 stars 2.74 score 11 scripts
zhaokg
Rbeast:Bayesian Change-Point Detection and Time Series Decomposition
Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.
Maintained by Kaiguang Zhao. Last updated 6 months ago.
anomoly-detectionbayesian-time-seriesbreakpoint-detectionchangepoint-detectioninterrupted-time-seriesseasonality-analysisstructural-breakpointtechnical-analysistime-seriestime-series-decompositiontrendtrend-analysis
3.5 match 302 stars 7.63 score 89 scripts
idem-lab
conmat:Builds Contact Matrices using GAMs and Population Data
Builds contact matrices using GAMs and population data. This package incorporates data that is copyright Commonwealth of Australia (Australian Electoral Commission and Australian Bureau of Statistics) 2020.
Maintained by Nicholas Tierney. Last updated 7 days ago.
contact-matricesinfectious-diseasespopulation-datapublic-health
3.5 match 19 stars 7.21 score 47 scripts
reconverse
outbreaks:A Collection of Disease Outbreak Data
Empirical or simulated disease outbreak data, provided either as RData or as text files.
Maintained by Finlay Campbell. Last updated 2 years ago.
3.5 match 51 stars 6.70 score 282 scriptsramikrispin
covid19italy:The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset
Provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile <https://www.protezionecivile.it/>.
Maintained by Rami Krispin. Last updated 2 years ago.
3.3 match 47 stars 6.07 score 25 scripts
hiroyukiyamamoto
mseapca:Metabolite Set Enrichment Analysis for Loadings
Computing metabolite set enrichment analysis (MSEA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>) and single sample enrichment analysis (SSEA) (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>).
Maintained by Hiroyuki Yamamoto. Last updated 4 months ago.
3.6 match 5 stars 4.32 score 14 scriptsepiforecasts
covidregionaldata:Subnational Data for COVID-19 Epidemiology
An interface to subnational and national level COVID-19 data sourced from both official sources, such as Public Health England in the UK, and from other COVID-19 data collections, including the World Health Organisation (WHO), European Centre for Disease Prevention and Control (ECDC), John Hopkins University (JHU), Google Open Data and others. Designed to streamline COVID-19 data extraction, cleaning, and processing from a range of data sources in an open and transparent way. This allows users to inspect and scrutinise the data, and tools used to process it, at every step. For all countries supported, data includes a daily time-series of cases. Wherever available data is also provided for deaths, hospitalisations, and tests. National level data are also supported using a range of sources.
Maintained by Sam Abbott. Last updated 3 years ago.
covid-19dataopen-sciencer6regional-data
1.7 match 37 stars 5.67 score 121 scripts
nau-ccl
SPARSEMODr:SPAtial Resolution-SEnsitive Models of Outbreak Dynamics
Implementation of spatially-explicit, stochastic disease models with customizable time windows that describe how parameter values fluctuate during outbreaks (e.g., in response to public health or conservation interventions).
Maintained by Joseph Mihaljevic. Last updated 3 years ago.
1.8 match 4.78 score 8 scripts
ganglilab
geneset:Get Gene Sets for Gene Enrichment Analysis
Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), 'KEGG' (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), 'WikiPathway' (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), 'MsigDb' (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), 'Reactome' (David et al. (2011) <doi:10.1093/nar/gkq1018>), 'MeSH' (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), 'DisGeNET' (Janet et al. (2017) <doi:10.1093/nar/gkw943>), 'Disease Ontology' (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), 'Network of Cancer Genes' (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and 'COVID-19' (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>). Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of terms ID and term description.
Maintained by Yunze Liu. Last updated 2 years ago.
enrichment-analysisgenegeneset-enrichment
1.5 match 9 stars 4.75 score 21 scripts 2 dependents
andeek
protoshiny:Interactive Dendrograms for Visualizing Hierarchical Clusters with Prototypes
Shiny app to interactively visualize hierarchical clustering with prototypes. For details on hierarchical clustering with prototypes, see Bien and Tibshirani (2011) <doi:10.1198/jasa.2011.tm10183>. This package currently launches the application.
Maintained by Andee Kaplan. Last updated 3 years ago.
2.3 match 1 stars 2.70 score 4 scripts
cran
GNAR:Methods for Fitting Network Time Series Models
Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables. Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>. Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.
Maintained by Matt Nunes. Last updated 6 months ago.
3.5 match 2 stars 1.30 score
hugofitipaldi
covidsymptom:COVID Symptom Study Sweden Open Dataset
The COVID Symptom Study is a non-commercial project that uses a free mobile app to facilitate real-time data collection of symptoms, exposures, and risk factors related to COVID19. The package allows easy access to summary statistics data from COVID Symptom Study Sweden.
Maintained by Hugo Fitipaldi. Last updated 11 months ago.
0.5 match 4 stars 4.30 score 7 scripts
thiyangtdata
covid19srilanka:The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data in Sri Lanka
Provides a daily counts of the Coronavirus (COVID19) cases by districts and country. Data source: Epidemiological Unit, Ministry of Health, Sri Lanka <https://www.epid.gov.lk/web/>.
Maintained by Thiyanga S. Talagala. Last updated 2 years ago.
0.5 match 1.70 score 7 scripts