Description: The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.

Description: Build friendly R packages that
praise their users if they have done something
good, or they just need it to feel better.
Collate: 'adjective.R' 'adverb.R' 'exclamation.R' 'verb.R' 'rpackage.R'
Packaged: 2015-08-11 02:01:43 UTC; gaborcsardi

Title: Kvantitatiivsete meetodite praktikumi asjad / Functions used in
the course "Quantitative methods in behavioural sciences"
Description: Kasulikud funktsioonid kvantitatiivsete mudelite kursuse

Title: Collection of Information-Based Feature Selection Filters
Description: A collection of feature selection filters performing greedy optimisation of mutual information-based usefulness criteria, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <http://www.jmlr.org/papers/v13/brown12a.html>.
Implements, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <http://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi  (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.

Title: Tools for Information-Based Feature Selection and Scoring
Description: A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>.
Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi  (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.
Author: Miron B. Kursa [aut, cre] (<https://orcid.org/0000-0001-7672-648X>)