Last data update: 2014.03.03

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Results 1 - 3 of 3 found.
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clusterSEs : Calculate Cluster-Robust p-Values and Confidence Intervals

Package: clusterSEs
Title: Calculate Cluster-Robust p-Values and Confidence Intervals
Version: 2.3.1
Authors@R: person("Justin", "Esarey", , "justin@justinesarey.com", role = c("aut", "cre"))
Description: Calculate p-values and confidence intervals using cluster-adjusted
t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.
Depends: R (>= 3.2.1), AER, Formula, plm, stats
Imports: sandwich, lmtest, mlogit, utils
License: GPL (>= 2)
LazyData: true
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-06-06 16:49:32 UTC; justi
Author: Justin Esarey [aut, cre]
Maintainer: Justin Esarey <justin@justinesarey.com>
Repository: CRAN
Date/Publication: 2016-06-06 20:36:32

● Data Source: CranContrib
● 0 images, 10 functions, 0 datasets
● Reverse Depends: 0

cquad : Conditional Maximum Likelihood for Quadratic Exponential Models for Binary Panel Data

Package: cquad
Type: Package
Title: Conditional Maximum Likelihood for Quadratic Exponential Models
for Binary Panel Data
Version: 1.3
Date: 2015-09-09
Author: Francesco Bartolucci (University of Perugia), Claudia Pigini (University of Ancona "Politecnica delle Marche")
Maintainer: Francesco Bartolucci <francesco.bartolucci@unipg.it>
Description: Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2013, Econometric Reviews). The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics).
License: GPL (>= 2)
Depends: R (>= 2.0.0), MASS, plm
Packaged: 2015-09-10 16:45:23 UTC; francescobartolucci
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-09-28 09:26:51

● Data Source: CranContrib
● 0 images, 10 functions, 1 datasets
● Reverse Depends: 0

pglm : panel generalized linear model

Package: pglm
Version: 0.1-2
Date: 2013-12-27
Title: panel generalized linear model
Authors@R: person("Yves", "Croissant", role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr")
Depends: R (>= 2.10), maxLik, plm
Imports: statmod
Suggests: lmtest, car
Description: Estimation of panel models for glm-like models: this includes binomial models (logit and probit) count models (poisson and negbin) and ordered models (logit and probit)
License: GPL (>= 2)
URL: http://www.r-project.org
Author: Yves Croissant [aut, cre]
Maintainer: Yves Croissant <yves.croissant@univ-reunion.fr>
Repository: CRAN
Repository/R-Forge/Project: pglm
Repository/R-Forge/Revision: 8
Repository/R-Forge/DateTimeStamp: 2013-12-28 07:37:40
Date/Publication: 2013-12-28 16:02:08
Packaged: 2013-12-28 11:15:17 UTC; rforge
NeedsCompilation: no

● Data Source: CranContrib
● 0 images, 1 functions, 4 datasets
● Reverse Depends: 0