- Referenced in 619 articles
- package SemiPar: Semiparametic Regression. The primary aim of this book is to guide researchers needing ... flexibly incorporate nonlinear relations into their regression analyses. Almost all existing regression texts treat either ... parametric or nonparametric regression exclusively. In this book the authors argue that nonparametric regression ... relatively simple extension of parametric regression and treat the two together. They refer to this...
- Referenced in 840 articles
- nonparametric curve estimation setting, namely density and regression estimation. Special attention is given...
- Referenced in 387 articles
- elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression...
- Referenced in 361 articles
- statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric...
- Referenced in 350 articles
- analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics...
- Referenced in 241 articles
- alr3: Data to accompany Applied Linear Regression 3rd edition , This package is a companion ... textbook S. Weisberg (2005), ”Applied Linear Regression,” 3rd edition, Wiley. It includes all the data...
- Referenced in 240 articles
- unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses ... popular S/S- PLUS language. Code for regression has been distributed in the R package...
- Referenced in 313 articles
- builds across multiple platforms, generating documentation and regression testing across a set of target platforms...
- Referenced in 254 articles
- pattern recognition, for the problem of regression, and for the problem of learning a ranking...
- Referenced in 138 articles
- Cutler’s random forests for classification and regression. Classification and regression based on a forest...
- Referenced in 136 articles
- procedure, based on a principle of ordinal regression, consist in solving a small linear program ... sensitivity analysis of the ordinal regression problem. There is a friendly user interface in which...
- Referenced in 128 articles
- CAViaR: Conditional autoregressive value at risk by regression quantiles. Value at risk ... autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period...
- Referenced in 161 articles
- nonconvex. It is also effective for nonlinear regression, problems with complementarity constraints (MPCCs or MPECs...
- Referenced in 98 articles
- Ordinal regression revisited: multiple criteria ranking with a set of additive value functions. VisualUTA ... value functions which result from an ordinal regression. The preference information provided by the decision ... alternatives. The preference model built via ordinal regression is a set of all additive value...
- Referenced in 111 articles
- package rpart: Recursive Partitioning. Recursive partitioning and regression trees. Recursive partitioning for classification, regression...
- Referenced in 108 articles
- least squares estimates for a univariate nonlinear regression model and generalized least squares estimates ... multivariate nonlinear regression model. Use the SUR option for both. Other options...
- Referenced in 74 articles
- package rms: Regression Modeling Strategies , Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting ... contains functions for binary and ordinal logistic regression models and the Buckley-James multiple regression ... linear models. rms works with almost any regression model, but it was especially written ... work with binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear...
- Referenced in 99 articles
- predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression...
- Referenced in 65 articles
- SVMTorch: Support vector machines for large-scale regression problems. Support Vector Machines (SVMs) for regression ... classification problems, but adapted to regression problems. With this algorithm, one can now efficiently solve ... large-scale regression problems (more than 20000 examples). Comparisons with Nodelib, another publicly available ... algorithm for large-scale regression problems from G. Flake and S. Lawrence [Mach. Learn...
- Referenced in 115 articles
- large-scale linear classification. It supports logistic regression and linear support vector machines. We provide...