R-universe search: exports:ns

Showing 7 of total 7 results (show query)

robinhankin

elliptic:Weierstrass and Jacobi Elliptic Functions

A suite of elliptic and related functions including Weierstrass and Jacobi forms. Also includes various tools for manipulating and visualizing complex functions.

Maintained by Robin K. S. Hankin. Last updated 27 days ago.

3 stars 9.31 score 54 scripts 79 dependents

nerler

JointAI:Joint Analysis and Imputation of Incomplete Data

Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.

Maintained by Nicole S. Erler. Last updated 12 months ago.

bayesian generalized-linear-models glm glmm imputation imputations jags joint-analysis linear-mixed-models linear-regression-models mcmc-sample mcmc-sampling missing-data missing-values survival cpp

28 stars 7.30 score 59 scripts 1 dependents

rivolli

utiml:Utilities for Multi-Label Learning

Multi-label learning strategies and others procedures to support multi- label classification in R. The package provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, pre-processing techniques and evaluation metrics. A complete overview of the matter can be seen in Zhang, M. and Zhou, Z. (2014) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and Ventura, S. (2015) A Tutorial on Multi-label Learning.

Maintained by Adriano Rivolli. Last updated 4 years ago.

28 stars 6.39 score 87 scripts

eu-ecdc

epitweetr:Early Detection of Public Health Threats from 'Twitter' Data

It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information is available in the 'epitweetr' peer-review publication (doi:10.2807/1560-7917.ES.2022.27.39.2200177).

Maintained by Laura Espinosa. Last updated 1 years ago.

early-warning-systems epidemic-surveillance lucene machine-learning signal-detection spark twitter

56 stars 5.98 score 86 scripts

daeyounglim

metapack:Bayesian Meta-Analysis and Network Meta-Analysis

Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) <doi:10.1002/sim.8983>. For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA.

Maintained by Daeyoung Lim. Last updated 1 years ago.

openblas cpp openmp

3 stars 4.18 score 10 scripts

billvenables

MASSExtra:Some 'MASS' Enhancements

Some enhancements, extensions and additions to the facilities of the recommended 'MASS' package that are useful mainly for teaching purposes, with more convenient default settings and user interfaces. Key functions from 'MASS' are imported and re-exported to avoid masking conflicts. In addition we provide some additional functions mainly used to illustrate coding paradigms and techniques, such as Gramm-Schmidt orthogonalisation and generalised eigenvalue problems.

Maintained by Bill Venables. Last updated 2 years ago.

2.74 score 11 scripts

techtonique

nnetsauce:Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Maintained by T. Moudiki. Last updated 7 months ago.

deep-learning machine-learning neural-networks randomized-algorithms statistical-learning

2 stars 2.60 score 6 scripts