Showing 14 of total 14 results (show query)
ropensci
nlrx:Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'
Setup, run and analyze 'NetLogo' (<https://ccl.northwestern.edu/netlogo/>) model simulations in 'R'. 'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using class objects. Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating 'NetLogo' experiments on remote high performance computing machines. In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.
Maintained by Sebastian Hanss. Last updated 7 months ago.
agent-based-modelingindividual-based-modellingnetlogopeer-reviewed
78 stars 8.86 score 195 scripts
cefet-rj-dal
harbinger:A Unified Time Series Event Detection Framework
By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.
Maintained by Eduardo Ogasawara. Last updated 4 months ago.
18 stars 8.32 score 216 scripts
biooss
sensitivity:Global Sensitivity Analysis of Model Outputs and Importance Measures
A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), for variable importance measures of data, as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs.
Maintained by Bertrand Iooss. Last updated 7 months ago.
17 stars 6.69 score 472 scripts 8 dependents
surmann
ODEsensitivity:Sensitivity Analysis of Ordinary Differential Equations
Performs sensitivity analysis in ordinary differential equation (ode) models. The package utilize the ode interface from 'deSolve' and connects it with the sensitivity analysis from 'sensitivity'. Additionally we add a method to run the sensitivity analysis on variables with class 'ODEnetwork'. A detailed plotting function provides outputs on the calculations. The method is described by Weber, Theers, Surmann, Ligges, and Weihs (2018) <doi:10.17877/DE290R-18874>.
Maintained by Dirk Surmann. Last updated 6 years ago.
6 stars 5.47 score 49 scripts
antonio-pgarcia
rrepast:Invoke 'Repast Simphony' Simulation Models
An R and Repast integration tool for running individual-based (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework directly from R code supporting multicore execution. This package integrates 'Repast Simphony' models within R environment, making easier the tasks of running and analyzing model output data for automated parameter calibration and for carrying out uncertainty and sensitivity analysis using the power of R environment.
Maintained by Antonio Prestes Garcia. Last updated 5 years ago.
3 stars 4.53 score 38 scripts 1 dependents
rikenbit
WormTensor:A Clustering Method for Time-Series Whole-Brain Activity Data of 'C. elegans'
A toolkit to detect clusters from distance matrices. The distance matrices are assumed to be calculated between the cells of multiple animals ('Caenorhabditis elegans') from input time-series matrices. Some functions for generating distance matrices, performing clustering, evaluating the clustering, and visualizing the results of clustering and evaluation are available. We're also providing the download function to retrieve the calculated distance matrices from 'figshare' <https://figshare.com>.
Maintained by Kentaro Yamamoto. Last updated 8 months ago.
1 stars 4.18 score 3 scriptsantonio-pgarcia
evoper:Evolutionary Parameter Estimation for 'Repast Simphony' Models
The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible package providing optimization driven parameter estimation methods using metaheuristics and evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization for continuous domains, Tabu Search, Evolutionary Strategies, ...) which could be more efficient and require, in some cases, fewer model evaluations than alternatives relying on experimental design. Currently there are built in support for models developed with 'Repast Simphony' Agent-Based framework (<https://repast.github.io/>) and with NetLogo (<https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.
Maintained by Antonio Prestes Garcia. Last updated 5 years ago.
6 stars 3.92 score 28 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
cran
multisensi:Multivariate Sensitivity Analysis
Functions to perform sensitivity analysis on a model with multivariate output.
Maintained by Hervé Monod. Last updated 7 years ago.
1 stars 2.00 score
sonjakuhnt
fanovaGraph:Building Kriging Models from FANOVA Graphs
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the 'RStudio IDE' with 'RStudio' package 'manipulate' loaded. 'RStudio' is freely available (<https://rstudio.com/>), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only. For further information on the method see Fruth, J., Roustant, O., Kuhnt, S. (2014) <doi:10.1016/j.jspi.2013.11.007>.
Maintained by Sonja Kuhnt. Last updated 4 years ago.
1 stars 1.20 score 16 scripts
santannaks
LSDsensitivity:Sensitivity Analysis Tools for LSD Simulations
Tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).
Maintained by Marcelo C. Pereira. Last updated 6 months ago.
1.04 score 11 scriptscran
sephora:Statistical Estimation of Phenological Parameters
Provides functions and methods for estimating phenological dates (green up, start of a season, maturity, senescence, end of a season and dormancy) from (nearly) periodic Earth Observation time series. These dates are critical points of some derivatives of an idealized curve which, in turn, is obtained through a functional principal component analysis-based regression model. Some of the methods implemented here are based on T. Krivobokova, P. Serra and F. Rosales (2022) <https://www.sciencedirect.com/science/article/pii/S0167947322000998>. Methods for handling and plotting Earth observation time series are also provided.
Maintained by Inder Tecuapetla-Gómez. Last updated 1 years ago.
1.00 scorecran
WOAkMedoids:Whale Optimization Algorithm for K-Medoids Clustering
Implements the Whale Optimization Algorithm(WOA) for k-medoids clustering, providing tools for effective and efficient cluster analysis in various data sets. The methodology is based on "The Whale Optimization Algorithm" by Mirjalili and Lewis (2016) <doi:10.1016/j.advengsoft.2016.01.008>.
Maintained by Chenan Huang. Last updated 7 months ago.
1.00 score