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alarm-redist

redist:Simulation Methods for Legislative Redistricting

Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>, the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>, the Merge-split/Recombination algorithms of Carter et al. (2019) <arXiv:1911.01503> and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst optimization algorithm of Cannon et al. (2020) <arXiv:2011.02288>.

Maintained by Christopher T. Kenny. Last updated 2 months ago.

geospatialgerrymanderingredistrictingsamplingopenblascppopenmp

69 stars 9.18 score 259 scripts

eth-mds

ricu:Intensive Care Unit Data with R

Focused on (but not exclusive to) data sets hosted on PhysioNet (<https://physionet.org>), 'ricu' provides utilities for download, setup and access of intensive care unit (ICU) data sets. In addition to functions for running arbitrary queries against available data sets, a system for defining clinical concepts and encoding their representations in tabular ICU data is presented.

Maintained by Nicolas Bennett. Last updated 10 months ago.

39 stars 5.65 score 77 scripts

fvafrcu

fakemake:Mock the Unix Make Utility

Use R as a minimal build system. This might come in handy if you are developing R packages and can not use a proper build system. Stay away if you can (use a proper build system).

Maintained by Andreas Dominik Cullmann. Last updated 2 years ago.

4.22 score 11 scripts 1 dependents

vcerqueira

autoBagging:Learning to Rank Bagging Workflows with Metalearning

A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.

Maintained by Vitor Cerqueira. Last updated 8 years ago.

1.70 score 9 scripts

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