R-universe search: topic:optimization

prioritizr

prioritizr:Systematic Conservation Prioritization in R

Systematic conservation prioritization using mixed integer linear programming (MILP). It provides a flexible interface for building and solving conservation planning problems. Once built, conservation planning problems can be solved using a variety of commercial and open-source exact algorithm solvers. By using exact algorithm solvers, solutions can be generated that are guaranteed to be optimal (or within a pre-specified optimality gap). Furthermore, conservation problems can be constructed to optimize the spatial allocation of different management actions or zones, meaning that conservation practitioners can identify solutions that benefit multiple stakeholders. To solve large-scale or complex conservation planning problems, users should install the Gurobi optimization software (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see Gurobi Installation Guide vignette for details). Users can also install the IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>). Additionally, the 'rcbc' R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). For further details, see Hanson et al. (2025) <doi:10.1111/cobi.14376>.

Maintained by Richard Schuster. Last updated 14 hours ago.

biodiversity conservation conservation-planner optimization prioritization solver spatial cpp

124 stars 11.71 score 584 scripts 2 dependents

mlr-org

mlr3tuning:Hyperparameter Optimization for 'mlr3'

Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling.

Maintained by Marc Becker. Last updated 3 months ago.

bbotk hyperparameter-optimization hyperparameter-tuning machine-learning mlr3 optimization tune tuning

55 stars 11.53 score 384 scripts 11 dependents

ardiad

DEoptim:Global Optimization by Differential Evolution

Implements the Differential Evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector as described in Mullen et al. (2011) <doi:10.18637/jss.v040.i06>.

Maintained by Katharine Mullen. Last updated 2 years ago.

differential-evolution evolutionary-algorithm global-optimization optimization

29 stars 11.42 score 680 scripts 124 dependents

florianhartig

BayesianTools:General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

General-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

Maintained by Florian Hartig. Last updated 1 years ago.

bayes ecological-models mcmc optimization smc systems-biology cpp

124 stars 10.18 score 580 scripts 5 dependents

mlr-org

bbotk:Black-Box Optimization Toolkit

Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect' and 'miesmuschel'.

Maintained by Marc Becker. Last updated 3 months ago.

bbotk black-box-optimization data-science hyperparameter-optimization hyperparameter-tuning machine-learning mlr3 optimization

22 stars 9.83 score 166 scripts 14 dependents

enricoschumann

NMOF:Numerical Methods and Optimization in Finance

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.

Maintained by Enrico Schumann. Last updated 1 months ago.

black-scholes differential-evolution genetic-algorithm grid-search heuristics implied-volatility local-search optimization particle-swarm-optimization simulated-annealing threshold-accepting

37 stars 9.40 score 101 scripts 3 dependents

yixuan

RcppNumerical:'Rcpp' Integration for Numerical Computing Libraries

A collection of open source libraries for numerical computing (numerical integration, optimization, etc.) and their integration with 'Rcpp'.

Maintained by Yixuan Qiu. Last updated 2 years ago.

integration numerical-methods optimization rcpp cpp

54 stars 9.00 score 21 scripts 33 dependents

canmod

macpan2:Fast and Flexible Compartmental Modelling

Fast and flexible compartmental modelling with Template Model Builder.

Maintained by Steve Walker. Last updated 5 days ago.

compartmental-models epidemiology forecasting mixed-effects model-fitting optimization simulation simulation-modeling cpp

4 stars 8.90 score 246 scripts 1 dependents

mlr-org

mlr3mbo:Flexible Bayesian Optimization

A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.

Maintained by Lennart Schneider. Last updated 24 days ago.

automl bayesian-optimization bbotk black-box-optimization gaussian-process hpo hyperparameter hyperparameter-optimization hyperparameter-tuning machine-learning mlr3 model-based-optimization optimization optimizer random-forest tuning

25 stars 8.57 score 120 scripts 3 dependents

dirkschumacher

ompr:Model and Solve Mixed Integer Linear Programs

Model mixed integer linear programs in an algebraic way directly in R. The model is solver-independent and thus offers the possibility to solve a model with different solvers. It currently only supports linear constraints and objective functions. See the 'ompr' website <https://dirkschumacher.github.io/ompr/> for more information, documentation and examples.

Maintained by Dirk Schumacher. Last updated 2 years ago.

integer-programming linear-programming milp mip optimization

268 stars 8.33 score 321 scripts 6 dependents

mlr-org

mlr3fselect:Feature Selection for 'mlr3'

Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.

Maintained by Marc Becker. Last updated 2 months ago.

evolutionary-algorithms exhaustive-search feature-selection machine-learning mlr3 optimization random-search recursive-feature-elimination sequential-feature-selection

23 stars 8.12 score 70 scripts 2 dependents

kingaa

subplex:Unconstrained Optimization using the Subplex Algorithm

The subplex algorithm for unconstrained optimization, developed by Tom Rowan.

Maintained by Aaron A. King. Last updated 5 months ago.

numerical-optimization optimization fortran openblas

10 stars 8.08 score 55 scripts 46 dependents

coatless-rpkg

RcppEnsmallen:Header-Only C++ Mathematical Optimization Library for 'Armadillo'

'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++14' and 'Armadillo' 10.8.2 or later.

Maintained by James Joseph Balamuta. Last updated 4 months ago.

armadillo cpp11 ensmallen optimization rcpp rcpparmadillo openblas cpp openmp

31 stars 7.67 score 1 scripts 14 dependents

dppalomar

riskParityPortfolio:Design of Risk Parity Portfolios

Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>. F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. <doi:10.2139/ssrn.2297383>. T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. <arXiv:1311.4057>.

Maintained by Daniel P. Palomar. Last updated 2 years ago.

optimization portfolio risk risk-parity cpp

108 stars 7.64 score 35 scripts 2 dependents

jakobbossek

ecr:Evolutionary Computation in R

Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.

Maintained by Jakob Bossek. Last updated 2 years ago.

combinatorial-optimization evolutionary-algorithm evolutionary-algorithms evolutionary-strategy genetic-algorithm-framework metaheuristics multi-objective-optimization optimization optimization-framework cpp

43 stars 7.36 score 89 scripts 2 dependents

mlr-org

mlr3hyperband:Hyperband for 'mlr3'

Successive Halving (Jamieson and Talwalkar (2016) <doi:10.48550/arXiv.1502.07943>) and Hyperband (Li et al. 2018 <doi:10.48550/arXiv.1603.06560>) optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk.

Maintained by Marc Becker. Last updated 9 months ago.

automl bbotk hyperband hyperparameter-tuning machine-learning mlr3 optimization tune tuning

18 stars 7.36 score 44 scripts 3 dependents

apariciojohan

flexFitR:Flexible Non-Linear Least Square Model Fitting

Provides tools for flexible non-linear least squares model fitting using general-purpose optimization techniques. The package supports a variety of optimization algorithms, including those provided by the 'optimx' package, making it suitable for handling complex non-linear models. Features include parallel processing support via the 'future' and 'foreach' packages, comprehensive model diagnostics, and visualization capabilities. Implements methods described in Nash and Varadhan (2011, <doi:10.18637/jss.v043.i09>).

Maintained by Johan Aparicio. Last updated 2 days ago.

nls optimization

2 stars 7.12 score 77 scripts

mhahsler

pomdp:Infrastructure for Partially Observable Markov Decision Processes (POMDP)

Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.

Maintained by Michael Hahsler. Last updated 4 months ago.

control-theory markov-decision-processes optimization cpp

19 stars 7.03 score 21 scripts

rte-antares-rpackage

antaresViz:Antares Visualizations

Visualize results generated by Antares, a powerful open source software developed by RTE to simulate and study electric power systems (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions that create interactive charts to help 'Antares' users visually explore the results of their simulations.

Maintained by Tatiana Vargas. Last updated 3 months ago.

adequacy bilan dygraphs electric energy leaflet linear-programming manipulatewidge monte-carlo-simulation optimization plotly previsionnel renewable-energy rte shiny shiny-apps simulation stochastic-simulation-algorithm tyndp

21 stars 6.83 score 32 scripts

jcrodriguez1989

rco:The R Code Optimizer

Automatically apply different strategies to optimize R code. 'rco' functions take R code as input, and returns R code as output.

Maintained by Juan Cruz Rodriguez. Last updated 5 months ago.

compiler fast gcc hpc optimization optimizer

82 stars 6.73 score

florianschwendinger

scs:Splitting Conic Solver

Solves convex cone programs via operator splitting. Can solve: linear programs ('LPs'), second-order cone programs ('SOCPs'), semidefinite programs ('SDPs'), exponential cone programs ('ECPs'), and power cone programs ('PCPs'), or problems with any combination of those cones. 'SCS' uses 'AMD' (a set of routines for permuting sparse matrices prior to factorization) and 'LDL' (a sparse 'LDL' factorization and solve package) from 'SuiteSparse' (<https://people.engr.tamu.edu/davis/suitesparse.html>).

Maintained by Florian Schwendinger. Last updated 2 years ago.

convex optimization openblas

8 stars 6.72 score 16 scripts 53 dependents

rte-antares-rpackage

antaresProcessing:'Antares' Results Processing

Process results generated by 'Antares', a powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors.

Maintained by Tatiana Vargas. Last updated 4 months ago.

infrastructure dataimport adequacy antares bilan datatable energy linear-algebra margins monte-carlo-simulation optimization previsionnel rte simulation surplus tyndp

8 stars 6.70 score 35 scripts 1 dependents

kerschke

flacco:Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems

Tools and features for "Exploratory Landscape Analysis (ELA)" of single-objective continuous optimization problems. Those features are able to quantify rather complex properties, such as the global structure, separability, etc., of the optimization problems.

Maintained by Pascal Kerschke. Last updated 2 years ago.

exploratory-landscape-analysis gui optimization

61 stars 6.70 score 41 scripts

roliveros-ramos

calibrar:Automated Parameter Estimation for Complex Models

General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as 'stats::optim()'. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See <https://roliveros-ramos.github.io/calibrar/> for more details.

Maintained by Ricardo Oliveros-Ramos. Last updated 2 days ago.

modeling optimization optimization-methods

7 stars 6.18 score 27 scripts

jakobbossek

grapherator:A Modular Multi-Step Graph Generator

Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights.

Maintained by Jakob Bossek. Last updated 3 years ago.

combinatorial-optimization graph-generator minimum-spanning-tree multi-objective-optimization optimization

9 stars 6.04 score 27 scripts 1 dependents

k3jph

cmna:Computational Methods for Numerical Analysis

Provides the source and examples for James P. Howard, II, "Computational Methods for Numerical Analysis with R," <https://jameshoward.us/cmna/>, a book on numerical methods in R.

Maintained by James Howard. Last updated 4 years ago.

bisection differential-equations heat-equation interpolation least-squares matrix-factorization monte-carlo newton numerical-analysis optimization partial-differential-equations quadrature root-finding secant splines testthat traveling-salesperson wave-equation

16 stars 5.65 score 62 scripts 3 dependents

mhahsler

markovDP:Infrastructure for Discrete-Time Markov Decision Processes (MDP)

Provides the infrastructure to work with Markov Decision Processes (MDPs) in R. The focus is on convenience in formulating MDPs, the support of sparse representations (using sparse matrices, lists and data.frames) and visualization of results. Some key components are implemented in C++ to speed up computation. Several popular solvers are implemented.

Maintained by Michael Hahsler. Last updated 15 days ago.

control-theory markov-decision-process optimization cpp

7 stars 5.51 score 4 scripts

blasif

cocons:Covariate-Based Covariance Functions for Nonstationary Spatial Modeling

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.

Maintained by Federico Blasi. Last updated 2 months ago.

covariance-matrix cpp estimation gaussian-processes large-dataset nonstationarity optimization prediction cpp

3 stars 5.48 score 1 scripts

prioriactions

prioriactions:Multi-Action Conservation Planning

This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the 'gurobi' installation vignette of the prioritizr package (available in <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, 'CPLEX' optimization solver can be obtain from IBM CPLEX web page (available here <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, the 'rcbc' R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>, Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015) <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. See the prioriactions website for more information, documentations and examples.

Maintained by Jose Salgado-Rojas. Last updated 2 years ago.

conservation conservation-plan optimization prioritization threats cpp

10 stars 5.40 score 6 scripts

boennecd

psqn:Partially Separable Quasi-Newton

Provides quasi-Newton methods to minimize partially separable functions. The methods are largely described by Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.

Maintained by Benjamin Christoffersen. Last updated 6 months ago.

optimization optimization-algorithms quasi-newton openblas cpp openmp

2 stars 5.26 score 5 scripts 3 dependents

ardiad

RiskPortfolios:Computation of Risk-Based Portfolios

Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.

Maintained by David Ardia. Last updated 4 years ago.

covariance optimization portfolio portfolio-optimization risk

51 stars 5.05 score 44 scripts

evolutionary-optimization-laboratory

rmoo:Multi-Objective Optimization in R

The 'rmoo' package is a framework for multi- and many-objective optimization, which allows researchers and users versatility in parameter configuration, as well as tools for analysis, replication and visualization of results. The 'rmoo' package was built as a fork of the 'GA' package by Luca Scrucca(2017) <DOI:10.32614/RJ-2017-008> and implementing the Non-Dominated Sorting Genetic Algorithms proposed by K. Deb's.

Maintained by Francisco Benitez. Last updated 5 months ago.

metaheuristics multiobjective multiobjective-optimization nsga nsga2 nsga3 optimization pareto-front

30 stars 5.01 score 23 scripts

ysosirius

windfarmGA:Genetic Algorithm for Wind Farm Layout Optimization

The genetic algorithm is designed to optimize wind farms of any shape. It requires a predefined amount of turbines, a unified rotor radius and an average wind speed value for each incoming wind direction. A terrain effect model can be included that downloads an 'SRTM' elevation model and loads a Corine Land Cover raster to approximate surface roughness.

Maintained by Sebastian Gatscha. Last updated 2 months ago.

windfarm-layout optimization genetic-algorithm renewable-energy cpp

29 stars 4.99 score 17 scripts

loelschlaeger

ao:Alternating Optimization

Alternating optimization is an iterative procedure that optimizes a function by alternately performing restricted optimization over individual parameter subsets. Instead of tackling joint optimization directly, it breaks the problem down into simpler sub-problems. This approach can make optimization feasible when joint optimization is too difficult.

Maintained by Lennart Oelschläger. Last updated 8 months ago.

optimization

2 stars 4.78 score 2 scripts

jatanrt

eprscope:Processing and Analysis of Electron Paramagnetic Resonance Data and Spectra in Chemistry

Processing, analysis and plottting of Electron Paramagnetic Resonance (EPR) spectra in chemistry. Even though the package is mainly focused on continuous wave (CW) EPR/ENDOR, many functions may be also used for the integrated forms of 1D PULSED EPR spectra. It is able to find the most important spectral characteristics like g-factor, linewidth, maximum of derivative or integral intensities and single/double integrals. This is especially important in spectral (time) series consisting of many EPR spectra like during variable temperature experiments, electrochemical or photochemical radical generation and/or decay. Package also enables processing of data/spectra for the analytical (quantitative) purposes. Namely, how many radicals or paramagnetic centers can be found in the analyte/sample. The goal is to evaluate rate constants, considering different kinetic models, to describe the radical reactions. The key feature of the package resides in processing of the universal ASCII text formats (such as '.txt', '.csv' or '.asc') from scratch. No proprietary formats are used (except the MATLAB EasySpin outputs) and in such respect the package is in accordance with the FAIR data principles. Upon 'reading' (also providing automatic procedures for the most common EPR spectrometers) the spectral data are transformed into the universal R 'data frame' format. Subsequently, the EPR spectra can be visualized and are fully consistent either with the 'ggplot2' package or with the interactive formats based on 'plotly'. Additionally, simulations and fitting of the isotropic EPR spectra are also included in the package. Advanced simulation parameters provided by the MATLAB-EasySpin toolbox and results from the quantum chemical calculations like g-factor and hyperfine splitting/coupling constants (a/A) can be compared and summarized in table-format in order to analyze the EPR spectra by the most effective way.

Maintained by Ján Tarábek. Last updated 16 hours ago.

chemistry data-analysis data-visualization epr esr fitting optimization programming-language reproducible-research scientific-plotting spectroscopy openjdk

4.76 score 7 scripts

loelschlaeger

optimizeR:Unified Framework for Numerical Optimizers

Provides a unified object-oriented framework for numerical optimizers in R. Allows for both minimization and maximization with any optimizer, optimization over more than one function argument, measuring of computation time, setting a time limit for long optimization tasks.

Maintained by Lennart Oelschläger. Last updated 20 hours ago.

optimization

4 stars 4.62 score 7 scripts 1 dependents

bioc

BioGA:Bioinformatics Genetic Algorithm (BioGA)

Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package allows users to analyze and optimize high throughput genomic data using genetic algorithms. The functions provided are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R.

Maintained by Dany Mukesha. Last updated 5 months ago.

experimentaldesign technology genetic-algorithm optimization cpp

4.54 score 5 scripts

gvegayon

ABCoptim:Implementation of Artificial Bee Colony (ABC) Optimization

An implementation of Karaboga (2005) Artificial Bee Colony Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>. This was developed upon the basic version programmed in C and available at the algorithm's official website.

Maintained by George Vega Yon. Last updated 2 years ago.

artificial-bee-colony optimization r-programming rcpp stochastic-optimizers cpp

30 stars 4.54 score 23 scripts

egeminiani

penfa:Single- And Multiple-Group Penalized Factor Analysis

Fits single- and multiple-group penalized factor analysis models via a trust-region algorithm with integrated automatic multiple tuning parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.

Maintained by Elena Geminiani. Last updated 4 years ago.

factor-analysis lasso latent-variables multiple-group optimization penalization psychometrics

3 stars 4.48 score 5 scripts

r-simmer

simmer.optim:Parameter Optimization Functions for 'simmer'

A set of optimization functions for variable optimization in simmer simulations.

Maintained by Bart Smeets. Last updated 2 years ago.

discrete-event optimization simulation

15 stars 4.35 score 4 scripts

konrad1991

paropt:Parameter Optimizing of ODE-Systems

Enable optimization of parameters of ordinary differential equations. Therefore, using 'SUNDIALS' to solve the ODE-System (see Hindmarsh, Alan C., Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol S. Woodward. (2005) <doi:10.1145/1089014.1089020>). Furthermore, for optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay Akman, and Elsa Schaefer. (2018) <doi:10.1155/2018/9160793> and Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. (2018) <doi:10.3390/make1010010>).

Maintained by Krämer Konrad. Last updated 9 months ago.

optimization paropt particle-swarm-optimization rcpp rcpparmadillo cpp

3 stars 4.26 score 12 scripts

bioc

MEIGOR:MEIGOR - MEtaheuristics for bIoinformatics Global Optimization

MEIGOR provides a comprehensive environment for performing global optimization tasks in bioinformatics and systems biology. It leverages advanced metaheuristic algorithms to efficiently search the solution space and is specifically tailored to handle the complexity and high-dimensionality of biological datasets. This package supports various optimization routines and is integrated with Bioconductor's infrastructure for a seamless analysis workflow.

Maintained by Jose A. Egea. Last updated 5 months ago.

systemsbiology optimization software

4.25 score 44 scripts

ngreifer

optweight:Targeted Stable Balancing Weights Using Optimization

Use optimization to estimate weights that balance covariates for binary, multinomial, and continuous treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.

Maintained by Noah Greifer. Last updated 2 years ago.

causal-inference inverse-probability-weights observational-study optimization propensity-scores

8 stars 3.78 score 15 scripts

crparedes

labsimplex:Simplex Optimization Algorithms for Laboratory and Manufacturing Processes

Simplex optimization algorithms as firstly proposed by Spendley et al. (1962) <doi:10.1080/00401706.1962.10490033> and later modified by Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> for laboratory and manufacturing processes. The package also provides tools for graphical representation of the simplexes and some example response surfaces that are useful in illustrating the optimization process.

Maintained by Cristhian Paredes. Last updated 4 years ago.

laboratory optimization simplex

1 stars 3.70 score 4 scripts

firefly-cpp

niarules:Numerical Association Rule Mining using Population-Based Nature-Inspired Algorithms

Framework is devoted to mining numerical association rules through the utilization of nature-inspired algorithms for optimization. Drawing inspiration from the 'NiaARM' 'Python' and the 'NiaARM' 'Julia' packages, this repository introduces the capability to perform numerical association rule mining in the R programming language. Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) <doi:10.1007/978-3-030-03493-1_9>.

Maintained by Iztok Jr. Fister. Last updated 24 days ago.

association-rules metaheuristics optimization

1 stars 3.70 score 2 scripts

act-org

RSCAT:Shadow-Test Approach to Computerized Adaptive Testing

As an advanced approach to computerized adaptive testing (CAT), shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically assembles entire shadow tests as a part of selecting items throughout the testing process. Selecting items from shadow tests guarantees the compliance of all content constraints defined by the blueprint. 'RSCAT' is an R package for the shadow-test approach to CAT. The objective of 'RSCAT' is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation; 2) Contributing to the academic and scientific community for CAT research. RSCAT is currently designed for dichotomous items based on the three-parameter logistic (3PL) model.

Maintained by Bingnan Jiang. Last updated 3 years ago.

computerized-adaptive-testing item-response-theory java mixed-integer-programming open-source optimization shadow-testing shiny xpress-mosel openjdk

7 stars 3.54 score 6 scripts

mhahsler

pomdpSolve:Interface to 'pomdp-solve' for Partially Observable Markov Decision Processes

Installs an updated version of 'pomdp-solve' and provides a low-level interface. Pomdp-solve is a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Kaelbling, Littman and Cassandra (1998) <doi:10.1016/S0004-3702(98)00023-X>.

Maintained by Michael Hahsler. Last updated 7 months ago.

control-theory markov-decision-processes optimization

2 stars 3.48 score 3 scripts 1 dependents

sustainscapes

TroublemakeR:Generates Spatial Problems in R for 'AMPL'

Provides methods for generating .dat files for use with the 'AMPL' software using spatial data, particularly rasters. It includes support for various spatial data formats and different problem types. By automating the process of generating 'AMPL' datasets, this package can help streamline optimization workflows and make it easier to solve complex optimization problems. The methods implemented in this package are described in detail in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).

Maintained by Derek Corcoran. Last updated 2 months ago.

optimization rpackge

3.18 score 4 scripts

david-cortes

nonneg.cg:Non-Negative Conjugate-Gradient Minimizer

Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in (Li, (2013) <https://www.hindawi.com/journals/jam/2013/986317/abs/>).

Maintained by David Cortes. Last updated 5 years ago.

conjugate-gradient minimize optimization openblas cpp openmp

2 stars 3.00 score 1 scripts

loelschlaeger

vntrs:Variable Neighborhood Trust Region Search

An implementation of the variable neighborhood trust region algorithm Bierlaire et al. (2009) "A Heuristic for Nonlinear Global Optimization" <doi:10.1287/ijoc.1090.0343>.

Maintained by Lennart Oelschläger. Last updated 1 years ago.

optimization

2.70 score 1 scripts

jasonjfoster

rolloptim:Rolling Optimizations

Analytical computation of rolling optimizations for time-series data.

Maintained by Jason Foster. Last updated 2 months ago.

algorithms optimization rcpp openblas cpp openmp

4 stars 2.60 score 4 scripts