R-universe search: exports:evaluate

r-lib

evaluate:Parsing and Evaluation Tools that Provide More Details than the Default

Parsing and evaluation tools that make it easy to recreate the command line behaviour of R.

Maintained by Hadley Wickham. Last updated 2 months ago.

evaluation parsing repl

135 stars 16.20 score 183 scripts 4.5k dependents

rstudio

tensorflow:R Interface to 'TensorFlow'

Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

Maintained by Tomasz Kalinowski. Last updated 3 days ago.

1.3k stars 15.47 score 3.2k scripts 75 dependents

r-lib

generics:Common S3 Generics not Provided by Base R Methods Related to Model Fitting

In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics.

Maintained by Hadley Wickham. Last updated 1 years ago.

61 stars 14.00 score 131 scripts 9.8k dependents

henrikbengtsson

R.utils:Various Programming Utilities

Utility functions useful when programming and developing R packages.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

63 stars 13.74 score 5.7k scripts 814 dependents

rstudio

keras3:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Maintained by Tomasz Kalinowski. Last updated 9 days ago.

845 stars 13.63 score 264 scripts 2 dependents

rspatial

dismo:Species Distribution Modeling

Methods for species distribution modeling, that is, predicting the environmental similarity of any site to that of the locations of known occurrences of a species.

Maintained by Robert J. Hijmans. Last updated 4 months ago.

cpp

25 stars 11.88 score 2.8k scripts 21 dependents

ropensci

drake:A Pipeline Toolkit for Reproducible Computation at Scale

A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://docs.ropensci.org/drake/> and the online manual <https://books.ropensci.org/drake/>.

Maintained by William Michael Landau. Last updated 4 months ago.

data-science drake high-performance-computing makefile peer-reviewed pipeline reproducibility reproducible-research ropensci workflow

1.3k stars 11.49 score 1.7k scripts 1 dependents

t-kalinowski

keras:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Maintained by Tomasz Kalinowski. Last updated 11 months ago.

10.93 score 10k scripts 55 dependents

zdebruine

RcppML:Rcpp Machine Learning Library

Fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.

Maintained by Zach DeBruine. Last updated 2 years ago.

clustering matrix-factorization nmf rcpp rcppeigen sparse-matrix cpp openmp

107 stars 10.66 score 125 scripts 50 dependents

henrikbengtsson

R.matlab:Read and Write MAT Files and Call MATLAB from Within R

Methods readMat() and writeMat() for reading and writing MAT files. For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.

Maintained by Henrik Bengtsson. Last updated 3 years ago.

matlab

85 stars 10.55 score 2.9k scripts 25 dependents

mhahsler

recommenderlab:Lab for Developing and Testing Recommender Algorithms

Provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.

Maintained by Michael Hahsler. Last updated 3 days ago.

collaborative-filtering recommender-system

214 stars 10.42 score 840 scripts 2 dependents

ludvigolsen

cvms:Cross-Validation for Model Selection

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

Maintained by Ludvig Renbo Olsen. Last updated 23 days ago.

39 stars 10.31 score 492 scripts 5 dependents

mlverse

luz:Higher Level 'API' for 'torch'

A high level interface for 'torch' providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to support expressing a large range of models. It's heavily inspired by 'fastai' by Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.

Maintained by Daniel Falbel. Last updated 6 months ago.

89 stars 9.86 score 318 scripts 4 dependents

eguidotti

calculus:High Dimensional Numerical and Symbolic Calculus

Efficient C++ optimized functions for numerical and symbolic calculus as described in Guidotti (2022) <doi:10.18637/jss.v104.i05>. It includes basic arithmetic, tensor calculus, Einstein summing convention, fast computation of the Levi-Civita symbol and generalized Kronecker delta, Taylor series expansion, multivariate Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators (Gradient, Jacobian, Hessian, Divergence, Curl, Laplacian) and numerical integration in arbitrary orthogonal coordinate systems: cartesian, polar, spherical, cylindrical, parabolic or user defined by custom scale factors.

Maintained by Emanuele Guidotti. Last updated 2 years ago.

calculus coordinate-systems curl divergence einstein finite-difference gradient hermite hessian jacobian laplacian numerical-derivation numerical-derivatives numerical-differentiation symbolic-computation symbolic-differentiation taylor cpp

47 stars 8.98 score 66 scripts 7 dependents

rstudio

tfestimators:Interface to 'TensorFlow' Estimators

Interface to 'TensorFlow' Estimators <https://www.tensorflow.org/guide/estimator>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.

Maintained by Tomasz Kalinowski. Last updated 3 years ago.

57 stars 8.42 score 170 scripts

robinhankin

hyper2:The Hyperdirichlet Distribution, Mark 2

A suite of routines for the hyperdirichlet distribution and reified Bradley-Terry; supersedes the 'hyperdirichlet' package; uses 'disordR' discipline <doi:10.48550/ARXIV.2210.03856>. To cite in publications please use Hankin 2017 <doi:10.32614/rj-2017-061>, and for Generalized Plackett-Luce likelihoods use Hankin 2024 <doi:10.18637/jss.v109.i08>.

Maintained by Robin K. S. Hankin. Last updated 3 hours ago.

cpp

5 stars 7.91 score 38 scripts 1 dependents

smac-group

simts:Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

rcpp rcpparmadillo simulation time-series timeseries timeseries-data openblas cpp

15 stars 7.68 score 59 scripts 4 dependents

mihaiconstantin

parabar:Progress Bar for Parallel Tasks

A simple interface in the form of R6 classes for executing tasks in parallel, tracking their progress, and displaying accurate progress bars.

Maintained by Mihai Constantin. Last updated 3 months ago.

parallel-computing progress-bar

20 stars 7.56 score 20 scripts 5 dependents

gagolews

FuzzyNumbers:Tools to Deal with Fuzzy Numbers

S4 classes and methods to deal with fuzzy numbers. They allow for computing any arithmetic operations (e.g., by using the Zadeh extension principle), performing approximation of arbitrary fuzzy numbers by trapezoidal and piecewise linear ones, preparing plots for publications, computing possibility and necessity values for comparisons, etc.

Maintained by Marek Gagolewski. Last updated 3 years ago.

10 stars 7.37 score 91 scripts 17 dependents

jacobbien

simulator:An Engine for Running Simulations

A framework for performing simulations such as those common in methodological statistics papers. The design principles of this package are described in greater depth in Bien, J. (2016) "The simulator: An Engine to Streamline Simulations," which is available at <arXiv:1607.00021>.

Maintained by Jacob Bien. Last updated 2 years ago.

simulation

52 stars 7.13 score 103 scripts

optad

adoptr:Adaptive Optimal Two-Stage Designs

Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.

Maintained by Maximilian Pilz. Last updated 6 months ago.

1 stars 7.09 score 39 scripts 1 dependents

sylvainschmitt

SSDM:Stacked Species Distribution Modelling

Allows to map species richness and endemism based on stacked species distribution models (SSDM). Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface.

Maintained by Sylvain Schmitt. Last updated 11 months ago.

44 stars 6.99 score 44 scripts

r-forge

modEvA:Model Evaluation and Analysis

Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.

Maintained by A. Marcia Barbosa. Last updated 9 days ago.

6.83 score 269 scripts 3 dependents

cefet-rj-dal

daltoolbox:Leveraging Experiment Lines to Data Analytics

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

Maintained by Eduardo Ogasawara. Last updated 2 months ago.

1 stars 6.65 score 536 scripts 4 dependents

bioc

struct:Statistics in R Using Class-based Templates

Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates.

Maintained by Gavin Rhys Lloyd. Last updated 5 months ago.

workflowstep

5.91 score 76 scripts 3 dependents

rebeccasalles

TSPred:Functions for Benchmarking Time Series Prediction

Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Maintained by Rebecca Pontes Salles. Last updated 4 years ago.

benchmarking linear-models machine-learning nonstationarity time-series-forecast time-series-prediction

24 stars 5.53 score 94 scripts 1 dependents

r-tensorflow

autokeras:R Interface to 'AutoKeras'

R Interface to 'AutoKeras' <https://autokeras.com/>. 'AutoKeras' is an open source software library for Automated Machine Learning (AutoML). The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. 'AutoKeras' provides functions to automatically search for architecture and hyperparameters of deep learning models.

Maintained by Juan Cruz Rodriguez. Last updated 4 years ago.

autodl automatic-machine-learning automl deep-learning keras machine-learning tensorflow

73 stars 5.34 score

annennenne

causalDisco:Tools for Causal Discovery on Observational Data

Various tools for inferring causal models from observational data. The package includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools for evaluating differences in adjacency matrices, which can be used for evaluating performance of causal discovery procedures.

Maintained by Anne Helby Petersen. Last updated 26 days ago.

19 stars 4.76 score 10 scripts

maxwestphal

cases:Stratified Evaluation of Subgroup Classification Accuracy

Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). <doi:10.1177/09622802241236933>).

Maintained by Max Westphal. Last updated 3 months ago.

1 stars 4.59 score 13 scripts

haghish

autoEnsemble:Automated Stacked Ensemble Classifier for Severe Class Imbalance

A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.

Maintained by E. F. Haghish. Last updated 5 days ago.

ai algorithm automated-machine-learning automl automl-algorithms ensemble ensemble-learning h2o h2oai machine-learning machinelearning metalearning stack-ensemble stacked-ensembles stacking

5 stars 4.42 score 21 scripts

klausherrmann

multIntTestFunc:Provides Test Functions for Multivariate Integration

Provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.

Maintained by Klaus Herrmann. Last updated 7 months ago.

4.30 score 1 scripts

martinbladt

matrixdist:Statistics for Matrix Distributions

Tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>, Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>, Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>, Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>, Bladt, M. (2022) <doi:10.1017/asb.2021.40>, Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>, Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>, Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

Maintained by Martin Bladt. Last updated 30 days ago.

openblas cpp openmp

1 stars 4.00 score 2 scripts

chencxxy28

InteRD:The Integrated and Robust Deconvolution

We developed the Integrated and Robust Deconvolution algorithm to infer cell-type proportions from target bulk RNA-seq data. This package is able to effectively integrate deconvolution results from multiple scRNA-seq datasets and calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Moreover, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system.

Maintained by Chixiang Chen. Last updated 3 years ago.

3.85 score 14 scripts

joycekang

symphony:Efficient and Precise Single-Cell Reference Atlas Mapping

Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.

Maintained by Joyce Kang. Last updated 2 years ago.

openblas cpp

3.83 score 134 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 25 days ago.

association-rules metaheuristics optimization

1 stars 3.70 score 2 scripts

r-forge

distrTEst:Estimation and Testing Classes Based on Package 'distr'

Evaluation (S4-)classes based on package distr for evaluating procedures (estimators/tests) at data/simulation in a unified way.

Maintained by Peter Ruckdeschel. Last updated 2 months ago.

3.68 score 3 scripts 1 dependents

mdbrown

TreatmentSelection:Evaluate Treatment Selection Biomarkers

A suite of descriptive and inferential methods designed to evaluate one or more biomarkers for their ability to guide patient treatment recommendations. Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) <doi:10.1515/ijb-2012-0052> for further details.

Maintained by Marshall Brown. Last updated 8 years ago.

3 stars 3.62 score 14 scripts

cran

randomizeR:Randomization for Clinical Trials

This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, 'randomizeR' provides a function for the comparison of randomization procedures.

Maintained by Ralf-Dieter Hilgers. Last updated 2 years ago.

2 stars 3.38 score 1 dependents

halpo

orthogonalsplinebasis:Orthogonal B-Spline Basis Functions

Represents the basis functions for B-splines in a simple matrix formulation that facilitates, taking integrals, derivatives, and making orthogonal the basis functions.

Maintained by Andrew Redd. Last updated 3 years ago.

1 stars 3.36 score 19 scripts 4 dependents

peterreichert

utility:Construct, Evaluate and Plot Value and Utility Functions

Construct and plot objective hierarchies and associated value and utility functions. Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables. Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies. Get numerical results of the evaluations in standard R data types for further processing.

Maintained by Peter Reichert. Last updated 2 years ago.

3.35 score 82 scripts 1 dependents

gertjanssenswillen

processpredictR:Process Prediction

Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple ways to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <arXiv:2104.00721>.

Maintained by Gert Janssenswillen. Last updated 2 years ago.

3.15 score 28 scripts

ymutua

mapsRinteractive:Local Adaptation and Evaluation of Raster Maps

Local adaptation and evaluation of maps of continuous attributes in raster format by use of point location data.

Maintained by Kristin Persson. Last updated 2 years ago.

2 stars 3.00 score 7 scripts

cran

symmoments:Symbolic Central and Noncentral Moments of the Multivariate Normal Distribution

Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.

Maintained by Kem Phillips. Last updated 5 years ago.

2.95 score 3 dependents

matt-dray

r2eng:Translate R Code To An English Sentence

Take an R expression and convert it to English by matching recognised symbols with an opinionated list of English 'translations'. Inspired By Amelia McNamara's useR! 2020 conference talk.

Maintained by Matt Dray. Last updated 11 months ago.

accessibility communication

17 stars 2.93 score 6 scripts

cullenpu

simpleMLP:Simple Multilayer Perceptron Neural Network

Create and train a multilayer perceptron, a type of feedforward, fully connected neural network. Features 2 ReLU hidden layers. Learn more about about the activation functions and backpropagation used by this network in Goodfellow et al. (2016, ISBN: 9780262035613) "Deep Learning".

Maintained by Cullen Pu. Last updated 4 years ago.

2.70 score 1 scripts

cran

BKT:Bayesian Knowledge Tracing Model

Fitting, cross-validating, and predicting with Bayesian Knowledge Tracing (BKT) models. It is designed for analyzing educational datasets to trace student knowledge over time. The package includes functions for fitting BKT models, evaluating their performance using various metrics, and making predictions on new data. It provides the similar functionality as the Python package pyBKT authored by Zachary A. Pardos (zp@berkeley.edu) at <https://github.com/CAHLR/pyBKT>.

Maintained by Yuhao Yuan. Last updated 2 months ago.

2.00 score

cran

DEXiR:'DEXi' Library

A software package for using 'DEXi' models. 'DEXi' models are hierarchical qualitative multi-criteria decision models developed according to the method DEX (Decision EXpert, <https://dex.ijs.si/documentation/DEX_Method/DEX_Method.html>), using the program 'DEXi' (<https://kt.ijs.si/MarkoBohanec/dexi.html>) or 'DEXiWin' (<https://dex.ijs.si/dexisuite/dexiwin.html>). A typical workflow with 'DEXiR' consists of: (1) reading a '.dxi' file, previously made using the 'DEXi' software (function read_dexi()), (2) making a data frame containing input values of one or more decision alternatives, (3) evaluating those alternatives (function evaluate()), (4) analyzing alternatives (selective_explanation(), plus_minus(), compare_alternatives()), (5) drawing charts. 'DEXiR' is restricted to using models produced externally by the 'DEXi' software and does not provide functionality for creating and/or editing 'DEXi' models directly in 'R'.

Maintained by Marko Bohanec. Last updated 6 months ago.

1.70 score

jfrench

gear:Geostatistical Analysis in R

Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).

Maintained by Joshua French. Last updated 5 years ago.

cpp

1.43 score 27 scripts

langema

sperich:Auxiliary Functions to Estimate Centers of Biodiversity

Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.

Maintained by Maximilian Lange. Last updated 2 years ago.

1.36 score 23 scripts

cran

adana:Adaptive Nature-Inspired Algorithms for Hybrid Genetic Optimization

The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package 'adana' includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).

Maintained by Erkut Tekeli. Last updated 3 years ago.

1.00 score

linusseelinger

umbridge:Integration for the UM-Bridge Protocol

A convenient wrapper for the UM-Bridge protocol. UM-Bridge is a protocol designed for coupling uncertainty quantification (or statistical / optimization) software to numerical models. A model is represented as a mathematical function with optional support for derivatives via Jacobian actions etc.

Maintained by Linus Seelinger. Last updated 3 years ago.

1.00 score 5 scripts