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3030130310 of 35883 results
62569.
GNU R package of Venables and Ripley's MASS
Summary
(no translation yet)
Located in Package: r-cran-mass
62570.
The MASS package provides functions and datasets to support the book 'Modern Applied Statistics with S' (4th edition) by W.N. Venables and B.D. Ripley. The following URL provides more details about the book: URL: http://www.stats.ox.ac.uk/pub/MASS4
Description
(no translation yet)
Located in Package: r-cran-mass
62571.
GNU R package of nonparametric matching methods
Summary
(no translation yet)
Located in Package: r-cran-matchit
62572.
MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2004) for improving parametric statistical models by preprocessing data with nonparametric matching methods.
Description
(no translation yet)
Located in Package: r-cran-matchit
62573.
MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly within Zelig.
Description
(no translation yet)
Located in Package: r-cran-matchit
62575.
This CRAN package provides S4 classes and methods for numerical linear algebra using dense or sparse matrices. The sparse matrix implementation uses code from the LDL sparse matrix package and code from the Metis package of partitioning algorithms.
Description
(no translation yet)
Located in Package: r-cran-matrix
62576.
R routines for Markov chain Monte Carlo model estimation
Summary
(no translation yet)
Located in Package: r-cran-mcmcpack
62577.
This is a set of routines for GNU R that implement various statistical and econometric models using Markov chain Monte Carlo (MCMC) estimation, which allows "solving" models that would otherwise be intractable with traditional techniques, particularly problems in Bayesian statistics (where one or more "priors" are used as part of the estimation procedure, instead of an assumption of ignorance about the "true" point estimates), although MCMC can also be used to solve frequentist statistical problems with uninformative priors. MCMC techniques are also preferable over direct estimation in the presence of missing data.
Description
(no translation yet)
Located in Package: r-cran-mcmcpack
62578.
Currently implemented are a number of ecological inference (EI) routines (for estimating individual-level attributes or behavior from aggregate data, such as electoral returns or census results), as well as models for traditional linear panel and cross-sectional data, some visualization routines for EI diagnostics, two item-response theory (or ideal-point estimation) models, metric, ordinal, and mixed-response factor analysis, and models for Gaussian (linear) and Poisson regression, logistic regression (or logit), and binary and ordinal-response probit models.
Description
(no translation yet)
Located in Package: r-cran-mcmcpack
62579.
The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.
Description
(no translation yet)
Located in Package: r-cran-mcmcpack
3030130310 of 35883 results

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