All Projects → mac-theobio → McMasterPandemic

mac-theobio / McMasterPandemic

Licence: GPL-3.0 license
SEIR+ model

Programming Languages

HTML
75241 projects
r
7636 projects
C++
36643 projects - #6 most used programming language
TeX
3793 projects
c
50402 projects - #5 most used programming language
Makefile
30231 projects

Projects that are alternatives of or similar to McMasterPandemic

Gluon Ts
Probabilistic time series modeling in Python
Stars: ✭ 2,373 (+13083.33%)
Mutual labels:  forecasting
Modeltime
Modeltime unlocks time series forecast models and machine learning in one framework
Stars: ✭ 189 (+950%)
Mutual labels:  forecasting
query-selector
LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION
Stars: ✭ 63 (+250%)
Mutual labels:  forecasting
Stocks
Programs for stock prediction and evaluation
Stars: ✭ 155 (+761.11%)
Mutual labels:  forecasting
Introduction To Time Series Forecasting Python
Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.
Stars: ✭ 173 (+861.11%)
Mutual labels:  forecasting
Da Rnn
Dual-Stage Attention-Based Recurrent Neural Net for Time Series Prediction
Stars: ✭ 242 (+1244.44%)
Mutual labels:  forecasting
Wayeb
Wayeb is a Complex Event Processing and Forecasting (CEP/F) engine written in Scala.
Stars: ✭ 138 (+666.67%)
Mutual labels:  forecasting
chronological-map-phenotypes
Machine-readable version of electronic health record phenotypes for Kuan V. and Denaxas S. et al.
Stars: ✭ 38 (+111.11%)
Mutual labels:  epidemiology
Supplychainpy
Supplychainpy is a Python library for supply chain analysis, modelling and simulation. The library assists a workflow that is reliant on Excel and VBA.
Stars: ✭ 184 (+922.22%)
Mutual labels:  forecasting
SARS-CoV-2-Nowcasting und -R-Schaetzung
Das Nowcasting erstellt eine Schätzung des Verlaufs der Anzahl von bereits erfolgten SARS-CoV-2-Erkrankungsfällen in Deutschland unter Berücksichtigung des Diagnose-, Melde- und Übermittlungsverzugs.
Stars: ✭ 80 (+344.44%)
Mutual labels:  forecasting
Java Timeseries
Time series analysis in Java
Stars: ✭ 155 (+761.11%)
Mutual labels:  forecasting
Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Stars: ✭ 13,832 (+76744.44%)
Mutual labels:  forecasting
anompy
A Python library for anomaly detection
Stars: ✭ 13 (-27.78%)
Mutual labels:  forecasting
Pyfts
An open source library for Fuzzy Time Series in Python
Stars: ✭ 154 (+755.56%)
Mutual labels:  forecasting
episuite
A suite of tools for epidemiology in Python.
Stars: ✭ 25 (+38.89%)
Mutual labels:  epidemiology
Forecasting
Time Series Forecasting Best Practices & Examples
Stars: ✭ 2,123 (+11694.44%)
Mutual labels:  forecasting
Tcdf
Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series
Stars: ✭ 217 (+1105.56%)
Mutual labels:  forecasting
wattnet-fx-trading
WATTNet: Learning to Trade FX with Hierarchical Spatio-Temporal Representations of Highly Multivariate Time Series
Stars: ✭ 70 (+288.89%)
Mutual labels:  forecasting
ForestCoverChange
Detecting and Predicting Forest Cover Change in Pakistani Areas Using Remote Sensing Imagery
Stars: ✭ 23 (+27.78%)
Mutual labels:  forecasting
dbnR
Gaussian dynamic Bayesian networks structure learning and inference based on the bnlearn package
Stars: ✭ 33 (+83.33%)
Mutual labels:  forecasting

McMasterPandemic

R-CMD-check Task list badge

Compartmental epidemic models for forecasting and analysis of infectious disease pandemics: contributions from Ben Bolker, Jonathan Dushoff, David Earn, Weiguang Guan, Morgan Kain, Michael Li, Irena Papst, Steve Walker (in alphabetical order). Feedback is welcome at the issues list, or e-mail us.

Refactoring for Speed and Generality

We are currently refactoring McMasterPandemic to make it faster and more general. We have merged the development branch for this refactoring project back into the master branch. However the classic functionality of McMasterPandemic is still available and coexists with the more general interface and faster engine. To get started with the classic functionality, please read this vignette. To get started with the faster and more general functionality, please read this user guide.

Documentation

Installation

The repository contains an R package and various workflows/analyses. This repository is not on CRAN so you will need to either fork/clone the repository (from here) or install directly from GitHub. Either option will (may?) require you to first install two R packages that are also not on CRAN.

remotes::install_github("bbolker/bbmle")
remotes::install_github("johndharrison/semver")

Note that these commands depend on having the remotes package, which you can get from CRAN with the following command from an R prompt.

install.packages('remotes')

To install McMasterPandemic itself you follow the same formula.

remotes::install_github("mac-theobio/McMasterPandemic")

If this command fails it may be because your R installation is not set up to compile C++ code. Windows users should be able to get past this issue by installing Rtools.

The classic McMasterPandemic functionality described here does not depend on C++ code, and you can get access to this functionality by installing with this command.

remotes::install_github("mac-theobio/[email protected]")

Getting access to experimental features can sometimes be achieved with this command.

remotes::install_github("mac-theobio/McMasterPandemic@tmb-condense")

DISCLAIMER

All use of this package is at your own risk. Quantitative forecasts are only as good as their parameter estimates.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].