Found 104 packages in 0.02 seconds
The R Interface to 'SyncroSim'
'SyncroSim' is a generalized framework for managing scenario-based datasets (< https://syncrosim.com/>). 'rsyncrosim' provides an interface to 'SyncroSim'. Simulation models can be added to 'SyncroSim' in order to transform these datasets, taking advantage of general features such as defining scenarios of model inputs, running Monte Carlo simulations, and summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 2.3.5 or higher (API documentation: < https://docs.syncrosim.com/>).
Power Analyses for Interaction Effects in Cross-Sectional Regressions
Power analysis for regression models which test the interaction of
two independent variables on a single dependent variable. Includes options
for continuous, binary, or ordinal variables, as well as correlated
interacting variables. Also includes options to specify variable reliability.
Power analyses can be done either analytically or via simulation. Includes
tools for simulating single data sets and visualizing power analysis results.
The primary functions are power_interaction_r2() and power_interaction().
Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR,
Olino TM (2022). "Tutorial: Power analyses for interaction effects in
cross-sectional regressions."
Nonparametric Models for Longitudinal Data
Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods for the conditional-mean and conditional-distribution based models with longitudinal Data.
Trajectories and Phylogenies Simulator
Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022)
Tools for Modelling of Animal Flight Performance
Allows estimation and modelling of flight costs in animal (vertebrate) flight,
implementing the aerodynamic power model described in Klein Heerenbrink et al.
(2015)
Lexicons for Text Analysis
A collection of lexical hash tables, dictionaries, and word lists.
Simulate from ODE-Based Models
Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.
Single Cell Transcriptomics-Level Cytokine Activity Prediction and Estimation
Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the 'CytoSig' and the 'Reactome' databases and scored using the modified 'Variance-adjusted Mahalanobis (VAM)' framework for single-cell RNA-sequencing (scRNA-seq) data. 'CytoSig' database is described in: Jiang at al., (2021)
Road Condition Analysis
Computation of the International Roughness Index (IRI) given a longitudinal road profile. The IRI can be calculated for a single road segment or for a sequence of segments with a fixed length (e. g. 100m). For the latter, an overlap of the segments can be selected. The IRI and likewise the algorithms for its determination are defined in Sayers, Michael W; Gillespie, Thomas D; Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) : establishing correlation and a calibration standard for measurements. World Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN 0-8213-0589-1) available from < http://documents.worldbank.org/curated/en/326081468740204115>.
sbioPN: Simulation of deterministic and stochastic spatial biochemical reaction networks using Petri Nets
sbioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction networks with spatial effects. Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined. For deterministic solutions, sbioPN creates the associated system of differential equations "on the fly", and solves it with a Runge Kutta Dormand Prince 45 explicit algorithm. For stochastic solutions, sbioPN offers two variants of Gillespie algorithm, or SSA. For hybrid deterministic/stochastic, it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions. sbioPN algorithms are developed in C to achieve adequate performance.