Showing 18 of total 18 results (show query)
tidymodels
tidymodels:Easily Install and Load the 'Tidymodels' Packages
The tidy modeling "verse" is a collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse.
Maintained by Max Kuhn. Last updated 1 months ago.
783 stars 16.52 score 66k scripts 15 dependents
moderndive
moderndive:Tidyverse-Friendly Introductory Linear Regression
Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.
Maintained by Albert Y. Kim. Last updated 3 months ago.
88 stars 11.32 score 1.8k scripts
business-science
modeltime:The Tidymodels Extension for Time Series Modeling
The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).
Maintained by Matt Dancho. Last updated 5 months ago.
arimadata-sciencedeep-learningetsforecastingmachine-learningmachine-learning-algorithmsmodeltimeprophettbatstidymodelingtidymodelstimetime-seriestime-series-analysistimeseriestimeseries-forecasting
551 stars 10.61 score 1.1k scripts 7 dependents
microsoft
finnts:Microsoft Finance Time Series Forecasting Framework
Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!
Maintained by Mike Tokic. Last updated 1 months ago.
businessdata-sciencefeature-selectionfinancefinntsforecastingmachine-learningmicrosofttime-series
194 stars 9.30 score 39 scripts
evolecolgroup
tidysdm:Species Distribution Models with Tidymodels
Fit species distribution models (SDMs) using the 'tidymodels' framework, which provides a standardised interface to define models and process their outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects, models and metrics specific to SDMs, as well as a number of specialised functions to process occurrences for contemporary and palaeo datasets. The full functionalities of the package are described in Leonardi et al. (2023) <doi:10.1101/2023.07.24.550358>.
Maintained by Andrea Manica. Last updated 26 days ago.
species-distribution-modellingtidymodels
31 stars 8.82 score 51 scriptsbusiness-science
modeltime.ensemble:Ensemble Algorithms for Time Series Forecasting with Modeltime
A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.
Maintained by Matt Dancho. Last updated 9 months ago.
ensembleensemble-learningforecastforecastingmodeltimestackingstacking-ensembletidymodelstimetime-seriestimeseries
77 stars 8.30 score 143 scripts
spsanderson
healthyR.ts:The Time Series Modeling Companion to 'healthyR'
Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.
Maintained by Steven Sanderson. Last updated 6 months ago.
aiarima-forecastingarima-modeletsforecastingggplot2machine-learningmodelingprophettime-seriestime-series-analysisworkflows
19 stars 7.58 score 56 scripts 1 dependentsspsanderson
healthyR.ai:The Machine Learning and AI Modeling Companion to 'healthyR'
Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.
Maintained by Steven Sanderson. Last updated 2 months ago.
aiartificial-intelligencehealthcareanalyticshealthyrhealthyversemachine-learning
16 stars 7.37 score 36 scripts 1 dependents
raymondbalise
rUM:R Templates from the University of Miami
This holds some r markdown and quarto templates and a template to create a research project in "R Studio".
Maintained by Raymond Balise. Last updated 11 days ago.
9 stars 6.84 score 16 scriptsbusiness-science
modeltime.resample:Resampling Tools for Time Series Forecasting
A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.
Maintained by Matt Dancho. Last updated 1 years ago.
accuracy-metricsbacktestingbootstrapbootstrappingcross-validationforecastingmodeltimemodeltime-resampleresamplingstatisticstidymodelstime-series
19 stars 6.64 score 38 scripts 1 dependents
statsgary
OddsPlotty:Odds Plot to Visualise a Logistic Regression Model
Uses the outputs of a logistic regression model, from caret <https://CRAN.R-project.org/package=caret>, to build an odds plot. This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model.
Maintained by Gary Hutson. Last updated 1 months ago.
17 stars 6.39 score 48 scripts 1 dependentsstatsgary
MLDataR:Collection of Machine Learning Datasets for Supervised Machine Learning
Contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains.
Maintained by Gary Hutson. Last updated 1 years ago.
53 stars 5.70 score 19 scriptsspsanderson
healthyverse:Easily Install and Load the 'healthyverse'
The 'healthyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'healthyverse' packages in a single step.
Maintained by Steven Sanderson. Last updated 6 months ago.
analyticshealthcarehealthcare-applicationinstallationinstallermetapackages
11 stars 5.12 score 24 scripts
bemts-hhs
traumar:Calculate Metrics for Trauma System Performance
Hospitals, hospital systems, and even trauma systems that provide care to injured patients may not be aware of robust metrics that can help gauge the efficacy of their programs in saving the lives of injured patients. 'traumar' provides robust functions driven by the academic literature to automate the calculation of relevant metrics to individuals desiring to measure the performance of their trauma center or even a trauma system. 'traumar' also provides some helper functions for the data analysis journey. Users can refer to the following publications for descriptions of the methods used in 'traumar'. TRISS methodology, including probability of survival, and the W, M, and Z Scores - Flora (1978) <doi:10.1097/00005373-197810000-00003>, Boyd et al. (1987, PMID:3106646), Llullaku et al. (2009) <doi:10.1186/1749-7922-4-2>, Singh et al. (2011) <doi:10.4103/0974-2700.86626>, Baker et al. (1974, PMID:4814394), and Champion et al. (1989) <doi:10.1097/00005373-198905000-00017>. For the Relative Mortality Metric, see Napoli et al. (2017) <doi:10.1080/24725579.2017.1325948>, Schroeder et al. (2019) <doi:10.1080/10903127.2018.1489021>, and Kassar et al. (2016) <doi:10.1177/00031348221093563>.
Maintained by Nicolas Foss. Last updated 8 days ago.
emsmortalitypiprobabilityqualitysurvivaltraumatriss
3 stars 4.08 score
alfrzlp
sae.projection:Small Area Estimation Using Model-Assisted Projection Method
Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.
Maintained by Ridson Al Farizal P. Last updated 2 months ago.
3.30 score 7 scripts
frankiethull
kantime:Nixtla's KAN Time Series Model In R
This package is a binding between Nixtla's neuralforecast Library, specifically KANs, and R's {modeltime} package. Nixtla's KAN is bound using {reticulate}, which is then ported into {parsnip} and bridged to {modeltime}.
Maintained by Frankie T. Hull. Last updated 3 months ago.
11 stars 3.04 score
rdinnager
sdmpack:FIU SDM Course Package
Course material for FIU course on SDM
Maintained by Russell Dinnage. Last updated 1 years ago.
2.08 score 24 scripts