Showing 200 of total 1608 results (show query)

topepo

C50:C5.0 Decision Trees and Rule-Based Models

C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).

Maintained by Max Kuhn. Last updated 2 years ago.

26.0 match 50 stars 11.99 score 1.3k scripts 13 dependents

tvganesh

QCSimulator:5 Qubit Quantum Computing Simulator

This package simulates a 5 qubit Quantum Computer.

Maintained by Tinniam V Ganesh. Last updated 9 years ago.

33.8 match 5 stars 4.20 score 64 scripts

r-lib

scales:Scale Functions for Visualization

Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.

Maintained by Thomas Lin Pedersen. Last updated 5 months ago.

ggplot2

4.9 match 419 stars 19.88 score 88k scripts 7.9k dependents

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

6.6 match 31 stars 11.46 score 474 scripts 25 dependents

dmurdoch

plotrix:Various Plotting Functions

Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.

Maintained by Duncan Murdoch. Last updated 1 years ago.

5.1 match 5 stars 11.31 score 9.2k scripts 361 dependents

mqbssppe

fabMix:Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <DOI:10.1198/jcgs.2010.09008>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).

Maintained by Panagiotis Papastamoulis. Last updated 1 years ago.

openblascppopenmp

23.4 match 2.09 score 41 scripts 1 dependents

prajual

bqror:Bayesian Quantile Regression for Ordinal Models

Package provides functions for estimating Bayesian quantile regression with ordinal outcomes, computing the covariate effects, model comparison measures, and inefficiency factor. The generic ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using Gibbs sampling only. For each model framework, there is a specific function for estimation. The summary output produces estimates for regression quantiles and two measures of model comparison — log of marginal likelihood and Deviance Information Criterion (DIC). The package also has specific functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, II(I): 1-24 <doi: 10.1214/15-BA939>. Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>. Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>. Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>. Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.

Maintained by Prajual Maheshwari. Last updated 3 years ago.

19.8 match 2.01 score 4 scripts

r-forge

stops:Structure Optimized Proximity Scaling

Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices.

Maintained by Thomas Rusch. Last updated 2 months ago.

openjdk

7.9 match 1 stars 4.48 score 23 scripts

pharmar

riskmetric:Risk Metrics to Evaluating R Packages

Facilities for assessing R packages against a number of metrics to help quantify their robustness.

Maintained by Eli Miller. Last updated 9 days ago.

3.4 match 167 stars 8.89 score 43 scripts

ropensci

readODS:Read and Write ODS Files

Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.

Maintained by Chung-hong Chan. Last updated 2 months ago.

cpp

2.3 match 55 stars 12.74 score 808 scripts 26 dependents

jpgattuso

seacarb:Seawater Carbonate Chemistry

Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.

Maintained by Jean-Pierre Gattuso. Last updated 1 years ago.

3.3 match 8 stars 8.27 score 350 scripts 5 dependents

mjlajeunesse

metagear:Comprehensive Research Synthesis Tools for Systematic Reviews and Meta-Analysis

Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 <doi:10.1111/2041-210X.12472>.

Maintained by Marc J. Lajeunesse. Last updated 4 years ago.

3.9 match 14 stars 6.71 score 91 scripts

mikejareds

hermiter:Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)

Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) <doi:10.1016/j.jmva.2021.104783>.

Maintained by Michael Stephanou. Last updated 7 months ago.

cumulative-distribution-functionkendall-correlation-coefficientonline-algorithmsprobability-density-functionquantilespearman-correlation-coefficientstatisticsstreaming-algorithmsstreaming-datacpp

4.7 match 15 stars 5.58 score 17 scripts