Showing 12 of total 12 results (show query)
n-kall
priorsense:Prior Diagnostics and Sensitivity Analysis
Provides functions for prior and likelihood sensitivity analysis in Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.
Maintained by Noa Kallioinen. Last updated 28 days ago.
bayesbayesianbayesian-data-analysisbayesian-methodsprior-distributionsensitivity-analysisstan
59 stars 8.27 score 70 scripts
dhaine
episensr:Basic Sensitivity Analysis of Epidemiological Results
Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Lash T.L, Fox M.P, and Fink A.K. "Applying Quantitative Bias Analysis to Epidemiologic Data", ('Springer', 2021).
Maintained by Denis Haine. Last updated 1 years ago.
biasepidemiologysensitivity-analysisstatistics
13 stars 6.48 score 39 scripts 1 dependents
paternogbc
sensiPhy:Sensitivity Analysis for Comparative Methods
An implementation of sensitivity analysis for phylogenetic comparative methods. The package is an umbrella of statistical and graphical methods that estimate and report different types of uncertainty in PCM: (i) Species Sampling uncertainty (sample size; influential species and clades). (ii) Phylogenetic uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty (intraspecific variation and measurement error).
Maintained by Gustavo Paterno. Last updated 5 years ago.
comparative-methodsecologyevolutionphylogeneticssensitivity-analysis
13 stars 6.38 score 61 scripts
sfcheung
semfindr:Influential Cases in Structural Equation Modeling
Sensitivity analysis in structural equation modeling using influence measures and diagnostic plots. Support leave-one-out casewise sensitivity analysis presented by Pek and MacCallum (2011) <doi:10.1080/00273171.2011.561068> and approximate casewise influence using scores and casewise likelihood.
Maintained by Shu Fai Cheung. Last updated 28 days ago.
diagnosticsinfluential-caseslavaanoutlier-detectionsensitivity-analysisstructural-equation-modeling
1 stars 6.03 score 90 scripts
paulgovan
PRA:Project Risk Analysis
Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, Learning Curves, Design Structure Matrices, and more.
Maintained by Paul Govan. Last updated 4 months ago.
causal-networkscontingency-analysismonte-carlo-simulationrisk-analysissensitivity-analysis
3 stars 5.82 score 8 scripts
nanhung
pksensi:Global Sensitivity Analysis in Physiologically Based Kinetic Modeling
Applying the global sensitivity analysis workflow to investigate the parameter uncertainty and sensitivity in physiologically based kinetic (PK) models, especially the physiologically based pharmacokinetic/toxicokinetic model with multivariate outputs. The package also provides some functions to check the convergence and sensitivity of model parameters. The workflow was first mentioned in Hsieh et al., (2018) <doi:10.3389/fphar.2018.00588>, then further refined (Hsieh et al., 2020 <doi:10.1016/j.softx.2020.100609>).
Maintained by Nan-Hung Hsieh. Last updated 4 months ago.
gnu-mcsimpharmacokineticssensitivitysensitivity-analysis
5 stars 5.64 score 88 scripts
angabrio
missingHE:Missing Outcome Data in Health Economic Evaluation
Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.
Maintained by Andrea Gabrio. Last updated 2 years ago.
cost-effectiveness-analysishealth-economic-evaluationindividual-level-datajagsmissing-dataparametric-modellingsensitivity-analysiscpp
5 stars 5.38 score 24 scripts
bertcarnell
tornado:Plots for Model Sensitivity and Variable Importance
Draws tornado plots for model sensitivity to univariate changes. Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package. Also draws variable importance plots.
Maintained by Rob Carnell. Last updated 8 months ago.
explanabilityregressionsensitivity-analysis
7 stars 4.85 score 4 scripts
mkoohafkan
reval:Argument Table Generation for Sensitivity Analysis
Simplified scenario testing and sensitivity analysis, redesigned to use packages 'future' and 'furrr'. Provides functions for generating function argument sets using one-factor-at-a-time (OFAT) and (sampled) permutations.
Maintained by Michael C Koohafkan. Last updated 7 months ago.
2 stars 4.04 score 11 scripts
mrcieu
tmsens:Sensitivity Analysis Using the Trimmed Means Estimator
Sensitivity analysis using the trimmed means estimator.
Maintained by Audinga-Dea Hazewinkel. Last updated 7 months ago.
missing-datasensitivity-analysistrimmed-means
1 stars 2.70 score 1 scripts
hongyuanjia
epluspar:Conduct Parametric Analysis on 'EnergyPlus' Models
A toolkit for conducting parametric analysis on 'EnergyPlus'(<https://energyplus.net>) models in R, including sensitivity analysis using Morris method and Bayesian calibration using using 'Stan'(<https://mc-stan.org>). References: Chong (2018) <doi:10.1016/j.enbuild.2018.06.028>.
Maintained by Hongyuan Jia. Last updated 1 years ago.
bayesian-calibrationenergyplusparametricsensitivity-analysiscpp
9 stars 2.65 score 4 scripts
maikol-solis
sobolnp:Nonparametric Sobol Estimator with Bootstrap Bandwidth
Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solís, M. (2018) <arXiv:1803.03333>.
Maintained by Maikol Solís. Last updated 2 years ago.
bandwidthbootstrapcross-validationnonparametric-regressionsensitivity-analysis
2.00 score 1 scripts