Showing 16 of total 16 results (show query)
r-forge
copula:Multivariate Dependence with Copulas
Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.
Maintained by Martin Maechler. Last updated 26 days ago.
11.83 score 1.2k scripts 86 dependents
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 4 days ago.
biodiversityconservationconservation-planneroptimizationprioritizationsolverspatialcpp
124 stars 11.71 score 584 scripts 2 dependents
bpfaff
vars:VAR Modelling
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Maintained by Bernhard Pfaff. Last updated 1 years ago.
7 stars 8.84 score 2.8k scripts 45 dependents
osofr
simcausal:Simulating Longitudinal Data with Causal Inference Applications
A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.
Maintained by Oleg Sofrygin. Last updated 9 months ago.
counterfactual-datasemsimulated-networksimulating-datastructural-equations
67 stars 7.06 score 170 scripts
ipd-tools
ipd:Inference on Predicted Data
Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2024) <doi:10.48550/arXiv.2410.09665>.
Maintained by Stephen Salerno. Last updated 3 months ago.
8 stars 6.13 score 5 scripts
igdawg
BIGDAWG:Case-Cotrol Analysis of Multi-Allelic Loci
Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.
Maintained by Steve Mack. Last updated 2 years ago.
3 stars 6.10 score 4 scripts 2 dependents
dppalomar
spectralGraphTopology:Learning Graphs from Data via Spectral Constraints
In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm.
Maintained by Ze Vinicius. Last updated 3 years ago.
2 stars 5.91 score 135 scripts 1 dependents
bioc
oligoClasses:Classes for high-throughput arrays supported by oligo and crlmm
This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages.
Maintained by Benilton Carvalho. Last updated 5 months ago.
5.86 score 93 scripts 17 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 18 days ago.
control-theorymarkov-decision-processoptimizationcpp
7 stars 5.51 score 4 scriptsbioc
crlmm:Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays
Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms.
Maintained by Benilton S Carvalho. Last updated 27 days ago.
microarraypreprocessingsnpcopynumbervariation
4.78 score 37 scripts 3 dependents
moondog1969
streamDAG:Analytical Methods for Stream DAGs
Provides indices and tools for directed acyclic graphs (DAGs), particularly DAG representations of intermittent streams. A detailed introduction to the package can be found in the publication: "Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG" (Aho et al., 2023) <doi:10.1016/j.envsoft.2023.105775>, and in the introductory package vignette.
Maintained by Ken Aho. Last updated 6 months ago.
1 stars 4.18 score 4 scriptsbioc
betaHMM:A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data
A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.
Maintained by Koyel Majumdar. Last updated 3 months ago.
dnamethylationdifferentialmethylationimmunooncologybiomedicalinformaticsmethylationarraysoftwaremultiplecomparisonsequencingspatialcoveragegenetargethiddenmarkovmodelmicroarray
4.18 score
dcousin3
ANOPA:Analyses of Proportions using Anscombe Transform
Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed data. The 'ANOPA' package can analyze proportions obtained from up to four factors. The factors can be within-subject or between-subject or a mix of within- and between-subject. The main, omnibus analysis can be followed by additive decompositions into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For that reason, we call this set of tools 'ANOPA' (Analysis of Proportion using Anscombe transform) to highlight its similarities with ANOVA. The 'ANOPA' framework also allows plots of proportions easy to obtain along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. Only particularity, the 'ANOPA' computes F statistics which have an infinite degree of freedom on the denominator. See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.
Maintained by Denis Cousineau. Last updated 2 months ago.
error-barsproportionsstatistical-testingstatisticssummary-statistics
1 stars 3.56 score 18 scripts
anespinosa
classicnets:classicnets: Classic Data of Social Networks
Classic Data of Social Networks. From the beginning of sociometry until the beginning of the new millenium.
Maintained by Alejandro Espinosa-Rada. Last updated 3 years ago.
network-analysisnetwork-sciencesocial-networksocial-network-analysis
11 stars 2.74 score 8 scripts
whzsdhr
LA:Lioness Algorithm (LA)
Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.
Maintained by Hongzhi Wang. Last updated 10 months ago.
2.00 score 5 scripts
