Showing 9 of total 9 results (show query)
csafe-isu
handwriter:Handwriting Analysis in R
Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: <https://github.com/CSAFE-ISU/handwriter>.
Maintained by Stephanie Reinders. Last updated 2 months ago.
24 stars 8.63 score 27 scripts 2 dependents
amices
ggmice:Visualizations for 'mice' with 'ggplot2'
Enhance a 'mice' imputation workflow with visualizations for incomplete and/or imputed data. The plotting functions produce 'ggplot' objects which may be easily manipulated or extended. Use 'ggmice' to inspect missing data, develop imputation models, evaluate algorithmic convergence, or compare observed versus imputed data.
Maintained by Hanne Oberman. Last updated 8 months ago.
32 stars 7.42 score 165 scripts
bstatcomp
bayes4psy:User Friendly Bayesian Data Analysis for Psychology
Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).
Maintained by Jure Demšar. Last updated 1 years ago.
14 stars 6.44 score 33 scripts
mrc-ide
drjacoby:Flexible Markov Chain Monte Carlo via Reparameterization
drjacoby is an R package for performing Bayesian inference via Markov chain monte carlo (MCMC). In addition to being highly flexible it implements some advanced techniques that can improve mixing in tricky situations.
Maintained by Bob Verity. Last updated 9 months ago.
12 stars 6.27 score 77 scripts
nicokubi
penetrance:Methods for Penetrance Estimation in Family-Based Studies
Implements statistical methods for estimating disease penetrance in family-based studies. Penetrance refers to the probability of disease§ manifestation in individuals carrying specific genetic variants. The package provides tools for age-specific penetrance estimation, handling missing data, and accounting for ascertainment bias in family studies. Cite as: Kubista, N., Braun, D. & Parmigiani, G. (2024) <doi:10.48550/arXiv.2411.18816>.
Maintained by Nicolas Kubista. Last updated 1 days ago.
5.43 score
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 scripts
priism-center
plotBart:Diagnostic and Plotting Functions to Supplement 'bartCause'
Functions to assist in diagnostics and plotting during the causal inference modeling process. Supplements the 'bartCause' package.
Maintained by Joseph Marlo. Last updated 10 months ago.
2 stars 4.30 score 20 scripts
ramiyaari
simode:Statistical Inference for Systems of Ordinary Differential Equations using Separable Integral-Matching
Implements statistical inference for systems of ordinary differential equations, that uses the integral-matching criterion and takes advantage of the separability of parameters, in order to obtain initial parameter estimates for nonlinear least squares optimization. Dattner & Yaari (2018) <arXiv:1807.04202>. Dattner et al. (2017) <doi:10.1098/rsif.2016.0525>. Dattner & Klaassen (2015) <doi:10.1214/15-EJS1053>.
Maintained by Rami Yaari. Last updated 5 years ago.
3 stars 4.18 score 4 scripts
gbradburd
BEDASSLE:Quantifies Effects of Geo/Eco Distance on Genetic Differentiation
Provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy.
Maintained by Gideon Bradburd. Last updated 1 years ago.
2 stars 3.26 score 30 scripts 1 dependents