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Epimed Solutions Collection for Data Editing, Analysis, and Benchmark of Health Units
Collection of functions related to benchmark with prediction models for data analysis and editing of clinical and epidemiological data.
A Comprehensive Collection of Cancer Types and Cancer-Related Datasets
Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.
Data Wrangling and Automated Reports from 'SIVIGILA' Source
Data wrangling, pre-processing, and generating automated reports from Colombia's epidemiological surveillance system, 'SIVIGILA' < https://portalsivigila.ins.gov.co/>. It provides a customizable R Markdown template for analysis and automatic generation of epidemiological reports that can be adapted to local, regional, and national contexts. This tool offers a standardized and reproducible workflow that helps to reduce manual labor and potential errors in report generation, improving their efficiency and consistency.
Simulating Phylogenetic Trees
Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.
Directed Acyclic Graphs: Analysis and Data Simulation
Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.
Supporting Functions Maintained by Zhen Lu
Miscellaneous functions commonly used by LuLab. This package aims to help more researchers on epidemiology to perform data management and visualization more efficiently.
Multiple Mediation Analysis
Used for general multiple mediation analysis.
The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017)
A Curated Collection of Pulmonary and Respiratory Disease Datasets
Provides a comprehensive and curated collection of datasets related to the lungs, respiratory system, and associated diseases. This package includes epidemiological, clinical, experimental, and simulated datasets on conditions such as lung cancer, asthma, Chronic Obstructive Pulmonary Disease (COPD), tuberculosis, whooping cough, pneumonia, influenza, and other respiratory illnesses. It is designed to support data exploration, statistical modeling, teaching, and research in pulmonary medicine, public health, environmental epidemiology, and respiratory disease surveillance.
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/>.
Converting Weekly Data to Monthly Data
Converts weekly data to monthly data. Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.