Showing 27 of total 27 results (show query)
rstudio
renv:Project Environments
A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.
Maintained by Kevin Ushey. Last updated 3 days ago.
1.0k stars 18.59 score 1.5k scripts 114 dependents
r-forge
deSolve:Solvers for Initial Value Problems of Differential Equations ('ODE', 'DAE', 'DDE')
Functions that solve initial value problems of a system of first-order ordinary differential equations ('ODE'), of partial differential equations ('PDE'), of differential algebraic equations ('DAE'), and of delay differential equations. The functions provide an interface to the FORTRAN functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK' collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk' and a C-implementation of solvers of the 'Runge-Kutta' family with fixed or variable time steps. The package contains routines designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D partial differential equations ('PDE') that have been converted to 'ODEs' by numerical differencing.
Maintained by Thomas Petzoldt. Last updated 1 years ago.
12.33 score 8.0k scripts 427 dependents
merliseclyde
BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Maintained by Merlise Clyde. Last updated 4 months ago.
bayesianbayesian-inferencegeneralized-linear-modelslinear-regressionlogistic-regressionmcmcmodel-selectionpoisson-regressionpredictive-modelingregressionvariable-selectionfortranopenblas
44 stars 10.63 score 420 scripts 3 dependents
bioc
methylumi:Handle Illumina methylation data
This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included.
Maintained by Sean Davis. Last updated 5 months ago.
dnamethylationtwochannelpreprocessingqualitycontrolcpgisland
9 stars 9.90 score 89 scripts 9 dependents
appsilon
rhino:A Framework for Enterprise Shiny Applications
A framework that supports creating and extending enterprise Shiny applications using best practices.
Maintained by Kamil Żyła. Last updated 21 hours ago.
305 stars 8.99 score 145 scripts
fbartos
RoBMA:Robust Bayesian Meta-Analyses
A framework for estimating ensembles of meta-analytic and meta-regression models (assuming either presence or absence of the effect, heterogeneity, publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to combine the competing meta-analytic models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual components (e.g., effect vs. no effect; Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022, <doi:10.1037/met0000405>). Users can define a wide range of prior distributions for + the effect size, heterogeneity, publication bias (including selection models and PET-PEESE), and moderator components. The package provides convenient functions for summary, visualizations, and fit diagnostics.
Maintained by František Bartoš. Last updated 2 months ago.
meta-analysismodel-averagingpublication-biasjagsopenblascpp
9 stars 6.88 score 53 scripts
kvasilopoulos
exuber:Econometric Analysis of Explosive Time Series
Testing for and dating periods of explosive dynamics (exuberance) in time series using the univariate and panel recursive unit root tests proposed by Phillips et al. (2015) <doi:10.1111/iere.12132> and Pavlidis et al. (2016) <doi:10.1007/s11146-015-9531-2>.The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes. Details can be found in Vasilopoulos et al. (2022) <doi:10.18637/jss.v103.i10>.
Maintained by Kostas Vasilopoulos. Last updated 1 years ago.
dickey-fullerexplosive-dynamicssimulationtime-seriesopenblascpp
29 stars 6.83 score 77 scripts
gsucarrat
gets:General-to-Specific (GETS) Modelling and Indicator Saturation Methods
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.
Maintained by Genaro Sucarrat. Last updated 8 months ago.
9 stars 6.79 score 73 scripts 3 dependents
jacolien
itsadug:Interpreting Time Series and Autocorrelated Data Using GAMMs
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).
Maintained by Jacolien van Rij. Last updated 3 years ago.
1 stars 6.45 score 576 scripts 2 dependents
cvasi-tktd
cvasi:Calibration, Validation, and Simulation of TKTD Models
Eases the use of ecotoxicological effect models. Can simulate common toxicokinetic-toxicodynamic (TK/TD) models such as General Unified Threshold models of Survival (GUTS) and Lemna. It can derive effects and effect profiles (EPx) from scenarios. It supports the use of 'tidyr' workflows employing the pipe symbol. Time-consuming tasks can be parallelized.
Maintained by Nils Kehrein. Last updated 10 days ago.
ecotoxicologymodelingsimulation
2 stars 6.27 score 12 scripts
rjdverse
rjd3toolkit:Utility Functions around 'JDemetra+ 3.0'
R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to model time series (create outlier regressors, user-defined calendar regressors, UCARIMA models...), to test the presence of trading days or seasonal effects and also to set specifications in pre-adjustment and benchmarking when using rjd3x13 or rjd3tramoseats.
Maintained by Tanguy Barthelemy. Last updated 5 months ago.
javajdemetraseasonal-adjustmenttime-seriestimeseriesopenjdk
6 stars 5.74 score 48 scripts 16 dependents
omniacsdao
Rnumerai:Interface to the Numerai Machine Learning Tournament API
Routines to interact with the Numerai Machine Learning Tournament API <https://numer.ai>. The functionality includes the ability to automatically download the current tournament data, submit predictions, and to get information for your user.
Maintained by Eric Hare. Last updated 3 years ago.
35 stars 5.53 score 39 scripts
biometris
statgenGxE:Genotype by Environment (GxE) Analysis
Analysis of multi environment data of plant breeding experiments following the analyses described in Malosetti, Ribaut, and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).
Maintained by Bart-Jan van Rossum. Last updated 6 months ago.
geneticsgxegxe-modellingmulti-trial-analysis
10 stars 5.53 score 17 scripts
avi-kenny
vaccine:Statistical Tools for Immune Correlates Analysis of Vaccine Clinical Trial Data
Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023) <doi:10.48550/arXiv.2208.06465>.
Maintained by Avi Kenny. Last updated 1 months ago.
4 stars 5.34 score 11 scripts
rich-payne
dreamer:Dose Response Models for Bayesian Model Averaging
Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) <doi:10.1002/bimj.201700211> for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) <doi:10.1080/10543406.2023.2292214>. Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019).
Maintained by Richard Daniel Payne. Last updated 3 months ago.
bayesiandose-response-modelingjagscpp
9 stars 5.26 score 5 scriptsfbartos
RoBTT:Robust Bayesian T-Test
An implementation of Bayesian model-averaged t-tests that allows users to draw inferences about the presence versus absence of an effect, variance heterogeneity, and potential outliers. The 'RoBTT' package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, <doi:10.3758/s13423-024-02590-5>). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, <doi:10.31234/osf.io/j9f3s>). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics.
Maintained by František Bartoš. Last updated 5 months ago.
bayesianmodel-averagingoutlierst-testcpp
3 stars 5.26 score 9 scripts
tilburgnetworkgroup
remstimate:Optimization Frameworks for Tie-Oriented and Actor-Oriented Relational Event Models
A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.
Maintained by Giuseppe Arena. Last updated 2 months ago.
5 stars 5.15 score 14 scripts
flr
FLXSA:eXtended Survivor Analysis for FLR
Calculates stock numbers and fishing mortality at age from commercial catch data and one or more indices of abundance suing the method in Darby and Flatman (1994) and Shepherd (1999).
Maintained by Iago Mosqueira. Last updated 4 months ago.
2 stars 4.36 score 95 scripts 1 dependents
sprfmo
jjmR:Graphics and diagnostics tools for SPRFMO's Joint Jack Mackerel model
Graphics and diagnostics tools for SPRFMO's Joint Jack Mackerel model.
Maintained by Ricardo Oliveros-Ramos. Last updated 6 months ago.
2 stars 3.81 score 12 scripts 1 dependentsfbartos
RoBSA:Robust Bayesian Survival Analysis
A framework for estimating ensembles of parametric survival models with different parametric families. The RoBSA framework uses Bayesian model-averaging to combine the competing parametric survival models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual predictors or preference for a parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>). The user can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, fit diagnostics, and prior distribution calibration.
Maintained by František Bartoš. Last updated 4 days ago.
bayesianmodel-averagingsurvival-analysisjagscpp
7 stars 3.54 score 1 scripts
nseg4
durhamSLR:The durhamSLR package
Data for Statistical Learning modules at Durham University.
Maintained by Sarah.Heaps. Last updated 2 years ago.
1.70 score
