R-universe search: exports:ev

Showing 6 of total 6 results (show query)

metrumresearchgroup

mrgsolve:Simulate from ODE-Based Models

Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.

Maintained by Kyle T Baron. Last updated 10 days ago.

mrgsolve ode openblas cpp

138 stars 10.90 score 1.2k scripts 3 dependents

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-networks contingency-analysis monte-carlo-simulation risk-analysis sensitivity-analysis

3 stars 5.82 score 8 scripts

bioc

antiProfiles:Implementation of gene expression anti-profiles

Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272.

Maintained by Hector Corrada Bravo. Last updated 5 months ago.

geneexpression classification

3.90 score

david-hammond

pmev:Calculates Earned Value for a Project Schedule

Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).

Maintained by David Hammond. Last updated 7 months ago.

3.30 score 4 scripts

cran

senstrat:Sensitivity Analysis for Stratified Observational Studies

Sensitivity analysis in unmatched observational studies, with or without strata. The main functions are sen2sample() and senstrat(). See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .

Maintained by Paul R. Rosenbaum. Last updated 8 years ago.

1.95 score 3 dependents

cran

iTOS:Methods and Examples from Introduction to the Theory of Observational Studies

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.

Maintained by Paul R. Rosenbaum. Last updated 7 months ago.

1.00 score