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relund

gMOIP:Tools for 2D and 3D Plots of Single and Multi-Objective Linear/Integer Programming Models

Make 2D and 3D plots of linear programming (LP), integer linear programming (ILP), or mixed integer linear programming (MILP) models with up to three objectives. Plots of both the solution and criterion space are possible. For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models (see vignettes for an overview). The package also contains an function for checking if a point is inside the convex hull.

Maintained by Lars Relund Nielsen. Last updated 5 months ago.

2d-plot3d-plotbi-objectiveconvex-hullinteger-programminglinear-programmingmathmilpmixed-integer-programmingmulti-objectivepolytopetri-objectivevisualization

24.8 match 5 stars 7.83 score 79 scripts 3 dependents

vfisikop

volesti:Volume Approximation and Sampling of Convex Polytopes

Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for estimating copulas useful in computational finance. Methods implemented in 'volesti' are described in A. Chalkis and V. Fisikopoulos (2022) <doi:10.32614/RJ-2021-077> and references therein.

Maintained by Vissarion Fisikopoulos. Last updated 5 months ago.

openblascpp

30.6 match 2 stars 2.84 score 69 scripts

danheck

multinomineq:Bayesian Inference for Multinomial Models with Inequality Constraints

Implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>. Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42–56, <doi:10.1037/a0021150>) or to perform outcome-based strategy classification to select the decision strategy that provides the best account for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26–40. <doi:10.1016/j.cogpsych.2017.05.003>).

Maintained by Daniel W. Heck. Last updated 1 years ago.

openblascppopenmp

12.3 match 4 stars 4.30 score 4 scripts

barnhilldave

TML:Tropical Geometry Tools for Machine Learning

Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) <arxiv:2209.15045>, Barnhill et al. (2023) <arxiv:2303.02539>, Barnhill and Yoshida (2023) <doi:10.3390/math11153433>, Aliatimis et al. (2023) <arXiv:2306.08796>, Yoshida et al. (2022) <arXiv:2206.04206>, and Yoshida et al. (2019) <doi:10.1007/s11538-018-0493-4>.

Maintained by David Barnhill. Last updated 8 months ago.

11.3 match 3 stars 3.65 score 1 scripts

gertvv

hitandrun:"Hit and Run" and "Shake and Bake" for Sampling Uniformly from Convex Shapes

The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) <doi:10.1016/j.ejor.2012.08.026>. van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) <doi:10.1016/j.ejor.2014.06.036>.

Maintained by Gert van Valkenhoef. Last updated 3 years ago.

openblas

3.7 match 16 stars 6.92 score 121 scripts 9 dependents

schloerke

geozoo:Zoo of Geometric Objects

Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.

Maintained by Barret Schloerke. Last updated 9 years ago.

4.5 match 5 stars 5.21 score 100 scripts 2 dependents

houjiewang

Rtropical:Data Analysis Tools over Space of Phylogenetic Trees Using Tropical Geometry

Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.

Maintained by Houjie Wang. Last updated 3 years ago.

5.4 match 4.00 score 5 scripts

dkahle

latte:Interface to 'LattE' and '4ti2'

Back-end connections to 'LattE' (<https://www.math.ucdavis.edu/~latte>) for counting lattice points and integration inside convex polytopes and '4ti2' (<http://www.4ti2.de/>) for algebraic, geometric, and combinatorial problems on linear spaces and front-end tools facilitating their use in the 'R' ecosystem.

Maintained by David Kahle. Last updated 2 years ago.

2.4 match 3 stars 3.18 score 1 scripts

cran

MCARtest:Optimal Nonparametric Testing of Missing Completely at Random

Provides functions for carrying out nonparametric hypothesis tests of the MCAR hypothesis based on the theory of Frechet classes and compatibility. Also gives functions for computing halfspace representations of the marginal polytope and related geometric objects.

Maintained by Thomas B. Berrett. Last updated 5 months ago.

cpp

5.7 match 1.30 score

antdambr

ConsRank:Compute the Median Ranking(s) According to the Kemeny's Axiomatic Approach

Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient. This release declare as deprecated some functions that are still in the package for compatibility. Next release will not contains these functions. Please type '?ConsRank-deprecated' Essential references: Emond, E.J., and Mason, D.W. (2002) <doi:10.1002/mcda.313>; D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) <doi:10.1285/i20705948v8n2p198>; Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) <doi:10.1016/j.ejor.2015.08.048>; D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) <doi:10.1016/j.cor.2017.01.017>; Albano, A., and Plaia, A. (2021) <doi:10.1285/i20705948v14n1p117>.

Maintained by Antonio DAmbrosio. Last updated 1 years ago.

1.6 match 3 stars 3.97 score 24 scripts 8 dependents

cran

polyapost:Simulating from the Polya Posterior

Simulate via Markov chain Monte Carlo (hit-and-run algorithm) a Dirichlet distribution conditioned to satisfy a finite set of linear equality and inequality constraints (hence to lie in a convex polytope that is a subset of the unit simplex).

Maintained by Glen Meeden. Last updated 6 months ago.

openblas

2.3 match 2.20 score 16 scripts

data-cleaning

lintools:Manipulation of Linear Systems of (in)Equalities

Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities.

Maintained by Mark van der Loo. Last updated 9 months ago.

0.5 match 4 stars 5.19 score 13 scripts 2 dependents

cran

rcdd:Computational Geometry

R interface to (some of) cddlib (<https://github.com/cddlib/cddlib>). Converts back and forth between two representations of a convex polytope: as solution of a set of linear equalities and inequalities and as convex hull of set of points and rays. Also does linear programming and redundant generator elimination (for example, convex hull in n dimensions). All functions can use exact infinite-precision rational arithmetic.

Maintained by Charles J. Geyer. Last updated 1 years ago.

gmp

0.5 match 4.63 score 35 dependents

cran

modQR:Multiple-Output Directional Quantile Regression

Contains basic tools for performing multiple-output quantile regression and computing regression quantile contours by means of directional regression quantiles. In the location case, one can thus obtain halfspace depth contours in two to six dimensions. Hallin, M., Paindaveine, D. and Šiman, M. (2010) Multivariate quantiles and multiple-output regression quantiles: from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669 For more references about the method, see Help pages.

Maintained by Pavel Boček. Last updated 3 years ago.

2.0 match 1.00 score

cran

LatticeDesign:Lattice-Based Space-Filling Designs

Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) <doi:10.1093/biomet/asx036>, interleaved lattice-based maximin distance designs proposed in Xu He (2018) <doi:10.1093/biomet/asy069>, interleaved lattice-based designs with low fill and high separation distance properties proposed in Xu He (2024) <doi:10.1137/23M156940X>, rotated sphere packing designs proposed in Xu He (2017) <doi:10.1080/01621459.2016.1222289>, sliced rotated sphere packing designs proposed in Xu He (2019) <doi:10.1080/00401706.2018.1458655>, and densest packing-based maximum projections designs proposed in Xu He (2021) <doi:10.1093/biomet/asaa057> and Xu He (2018) <doi:10.48550/arXiv.1709.02062>.

Maintained by Xu He. Last updated 1 months ago.

1.8 match 1.00 score

cran

tmvmixnorm:Sampling from Truncated Multivariate Normal and t Distributions

Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong. Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed. Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).

Maintained by Ting Fung (Ralph) Ma. Last updated 4 years ago.

0.5 match 2.18 score 5 dependents

pavlomozharovskyi

TukeyRegion:Tukey Region and Median

Tukey regions are polytopes in the Euclidean space, viz. upper-level sets of the Tukey depth function on given data. The bordering hyperplanes of a Tukey region are computed as well as its vertices, facets, centroid, and volume. In addition, the Tukey median set, which is the non-empty Tukey region having highest depth level, and its barycenter (= Tukey median) are calculated. Tukey regions are visualized in dimension two and three. For details see Liu, Mosler, and Mozharovskyi (2019, <doi:10.1080/10618600.2018.1546595>). See file LICENSE.note for additional license information.

Maintained by Pavlo Mozharovskyi. Last updated 2 years ago.

cpp

0.5 match 1.00 score

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