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3520135210 of 41567 results
67661.
MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2004) for improving parametric statistical models by preprocessing data with nonparametric matching methods.
Description
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Located in Package: r-cran-matchit
67662.
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
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Located in Package: r-cran-matchit
67664.
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
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Located in Package: r-cran-matrix
67665.
R routines for Markov chain Monte Carlo model estimation
Summary
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Located in Package: r-cran-mcmcpack
67666.
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
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Located in Package: r-cran-mcmcpack
67667.
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
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Located in Package: r-cran-mcmcpack
67668.
The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.
Description
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Located in Package: r-cran-mcmcpack
67669.
GNU R Medication Adherence: Commonly Used Definitions
Summary
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Located in Package: r-cran-medadherence
67670.
Adherence is defined as "the extent to which a person's behavior coincides with medical or health advice", which is very important, for both clinical researchers and physicians, to identify the treatment effect of a specific medication(s).
Description
(no translation yet)
Located in Package: r-cran-medadherence
67671.
A variety of measures have been developed to calculate the medication adherence. Definitions and methods to address adherence differ greatly in public literature. Choosing which definition should be determined by overall study goals. This package provides the functions to calculate medication adherence based on commonly used definitions.
Description
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Located in Package: r-cran-medadherence
3520135210 of 41567 results

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