Showing 12 of total 12 results (show query)
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
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 22 days ago.
species-distribution-modellingtidymodels
31 stars 8.82 score 51 scripts
tidymodels
censored:'parsnip' Engines for Survival Models
Engines for survival models from the 'parsnip' package. These include parametric models (e.g., Jackson (2016) <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g., Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).
Maintained by Hannah Frick. Last updated 1 months ago.
122 stars 8.68 score 254 scripts 1 dependentsbusiness-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 8 months ago.
ensembleensemble-learningforecastforecastingmodeltimestackingstacking-ensembletidymodelstimetime-seriestimeseries
77 stars 8.30 score 143 scripts
hsbadr
bayesian:Bindings for Bayesian TidyModels
Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' <doi:10.5281/zenodo.4426836>. 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>). The technical details of 'brms' and 'Stan' are described in Bürkner (2017) <doi:10.18637/jss.v080.i01>, Bürkner (2018) <doi:10.32614/RJ-2018-017>, and Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Hamada S. Badr. Last updated 2 months ago.
46 stars 7.47 score 18 scripts
corybrunson
ordr:A Tidyverse Extension for Ordinations and Biplots
Ordination comprises several multivariate exploratory and explanatory techniques with theoretical foundations in geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. Greenacre (2010, ISBN:978-84-923846) shows how the most established of these, including principal components analysis, correspondence analysis, multidimensional scaling, factor analysis, and discriminant analysis, rely on eigen-decompositions or singular value decompositions of pre-processed numeric matrix data. These decompositions give rise to a set of shared coordinates along which the row and column elements can be measured. The overlay of their scatterplots on these axes, introduced by Gabriel (1971) <doi:10.1093/biomet/58.3.453>, is called a biplot. 'ordr' provides inspection, extraction, manipulation, and visualization tools for several popular ordination classes supported by a set of recovery methods. It is inspired by and designed to integrate into 'tidyverse' workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.
Maintained by Jason Cory Brunson. Last updated 25 days ago.
biplotdata-visualizationdimension-reductiongeometric-data-analysisgrammar-of-graphicslog-ratio-analysismultivariate-analysismultivariate-statisticsordinationtidymodelstidyverse
24 stars 7.26 score 28 scripts
ropensci
waywiser:Ergonomic Methods for Assessing Spatial Models
Assessing predictive models of spatial data can be challenging, both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors, with "hot spots" of higher than expected error clustered geographically due to spatial structure in the underlying data. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the 'tidymodels' framework. Methods include Moran's I ('Moran' (1950) <doi:10.2307/2332142>), Geary's C ('Geary' (1954) <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from 'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>) and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an implementation of the area of applicability methodology from 'Meyer' and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an implementation of multi-scale assessment as described in 'Riemann' 'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).
Maintained by Michael Mahoney. Last updated 12 days ago.
spatialspatial-analysistidymodelstidyverse
37 stars 6.93 score 19 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
spsanderson
tidyAML:Automatic Machine Learning with 'tidymodels'
The goal of this package will be to provide a simple interface for automatic machine learning that fits the 'tidymodels' framework. The intention is to work for regression and classification problems with a simple verb framework.
Maintained by Steven Sanderson. Last updated 11 months ago.
automatic-machine-learningautomlclassificationmachine-learningparsnipr-languager-programmingregressiontidytidymodelstidyverse
68 stars 6.56 score 36 scripts 1 dependentshsbadr
additive:Bindings for Additive TidyModels
Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels' via 'additive' <doi:10.5281/zenodo.4784245>. 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>). The technical details of 'mgcv' are described in Wood (2017) <doi:10.1201/9781315370279>.
Maintained by Hamada S. Badr. Last updated 2 months ago.
additivebamgamgeneralized-additive-modelsmgcvtidymodels
7 stars 5.99 score 14 scripts
jameshwade
measure:A Recipes-style Interface to Tidymodels for Analytical Measurements
Analytical measurements...
Maintained by James Wade. Last updated 1 months ago.
5 stars 5.06 score 58 scripts
paithiov909
baritsu:Wrappers for 'mlpack'
A collection of wrappers for the 'mlpack' package that allows passing formula as their argument.
Maintained by Akiru Kato. Last updated 1 months ago.
3 stars 3.08 score 1 scripts