Showing 7 of total 7 results (show query)
epiforecasts
EpiNow2:Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters
Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported.
Maintained by Sebastian Funk. Last updated 1 months ago.
backcalculationcovid-19gaussian-processesopen-sourcereproduction-numberstancpp
123 stars 11.86 score 210 scripts
alexpghayes
distributions3:Probability Distributions as S3 Objects
Tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.
Maintained by Alex Hayes. Last updated 6 months ago.
102 stars 11.35 score 118 scripts 7 dependents
epinowcast
epidist:Estimate Epidemiological Delay Distributions With brms
Understanding and accurately estimating epidemiological delay distributions is important for public health policy. These estimates influence epidemic situational awareness, control strategies, and resource allocation. This package provides methods to address the key challenges in estimating these distributions, including truncation, interval censoring, and dynamical biases. These issues are frequently overlooked, resulting in biased conclusions. Built on top of 'brms', it allows for flexible modelling including time-varying spatial components and partially pooled estimates of demographic characteristics.
Maintained by Sam Abbott. Last updated 22 days ago.
14 stars 6.52 score 7 scripts
silvaneojunior
kDGLM:Bayesian Analysis of Dynamic Generalized Linear Models
Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<doi:10.48550/arXiv.2201.05387> and dos Santos Jr. et al. (2024)<doi:10.48550/arXiv.2403.13069>.
Maintained by Silvaneo dos Santos Jr.. Last updated 11 days ago.
2 stars 5.70 score 9 scripts
yrobink
ROOPSD:R Object Oriented Programming for Statistical Distribution
Statistical distribution in OOP (Object Oriented Programming) way. This package proposes a R6 class interface to classic statistical distribution, and new distributions can be easily added with the class AbstractDist. A useful point is the generic fit() method for each class, which uses a maximum likelihood estimation to find the parameters of a dataset, see, e.g. Hastie, T. and al (2009) <isbn:978-0-387-84857-0>. Furthermore, the rv_histogram class gives a non-parametric fit, with the same accessors that for the classic distribution. Finally, three random generators useful to build synthetic data are given: a multivariate normal generator, an orthogonal matrix generator, and a symmetric positive definite matrix generator, see Mezzadri, F. (2007) <arXiv:math-ph/0609050>.
Maintained by Yoann Robin. Last updated 2 years ago.
1 stars 4.49 score 5 scripts 12 dependents
cran
BayesFluxR:Implementation of Bayesian Neural Networks
Implementation of 'BayesFlux.jl' for R; It extends the famous 'Flux.jl' machine learning library to Bayesian Neural Networks. The goal is not to have the fastest production ready library, but rather to allow more people to be able to use and research on Bayesian Neural Networks.
Maintained by Enrico Wegner. Last updated 1 years ago.
1.70 score
jahmadkhan
DELTD:Kernel Density Estimation using Lifetime Distributions
A collection of asymmetrical kernels belong to lifetime distributions for kernel density estimation is presented. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000a,b), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>.
Maintained by Javaria Ahmad Khan. Last updated 3 years ago.
1.48 score 1 dependents