Authors@R: c(person("Mar", "Llaberia-Robledillo", role = c("aut", "cre", "cph"), email = "mar.llaberia@uv.es", comment = c(ORCID = "0000-0002-7989-796X")),
person("Juan A.", "Balbuena", role = c("aut", "cph"), email = "j.a.balbuena@uv.es", comment = c(ORCID = "0000-0003-4006-1353"))
Description: Applies a given global-fit method to random partial tanglegrams of a fixed size
to identify the associations, terminals, and nodes that maximize phylogenetic
(in)congruence. It also includes functions to compute more easily the confidence
intervals of classification metrics and plot results, reducing computational time.
Depends: phytools, parallel, R (>= 3.5.0)
Imports: ape, distory, GiniWegNeg, paco, stats, vegan, stringr,
Author: Mar Llaberia-Robledillo [aut, cre, cph]
Juan A. Balbuena [aut, cph] (<https://orcid.org/0000-0003-4006-1353>)

person("Juan A.", "Balbuena", role = c("aut", "cph"), email = "j.a.balbuena@uv.es", comment = c(ORCID = "0000-0003-4006-1353")),
person("José Ignacio", "Lucas-Lledó", role = c("aut", "cph"), comment = c(ORCID = "0000-0001-6254-8942")),
person("Oscar Alejandro", "Pérez-Escobar", role = c("aut", "cph"), comment = c(ORCID = "0000-0001-9166-2410"))
Oscar Alejandro Pérez-Escobar [aut, cph]

Title: Discretization of AR(1) Processes
Author: David Zarruk Valencia & Rodrigo Azuero Melo
Maintainer: David Zarruk Valencia <davidzarruk@gmail.com>
Description: Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.
Packaged: 2016-08-07 19:50:26 UTC; rodrigoazuero

Title: Summarizes Text by Extracting Relevant Sentences
person("Suryavanshi", "Abhijit", role = c("aut", "cre"),
Description: Build a text summary by extracting relevant sentences from your text. The training dataset should consist of several documents, each document should have sentences separated by a period. While fitting the model, the 'term frequency - inverse document frequency' (TF-IDF) matrix that reflects how important a word is to a document is calculated first. Then vector representations for words are obtained from the 'global vectors for word representation' algorithm (GloVe). While applying the model on new data, the GloVe word vectors for each word are weighted by their TF-IDF weights and averaged to give a sentence vector or a document vector. The magnitude of this sentence vector gives the importance of that sentence within the document. Another way to obtain the importance of the sentence is to calculate cosine similarity between the sentence vector and the document vector. The output can either be at the sentence level (sentences and weights are returned) or at a document level (the summary for each document is returned). It is useful to first get a sentence level output and get quantiles of the sentence weights to determine a cutoff threshold for the weights. This threshold can then be used in the document level output. This method is a variation of the TF-IDF extractive summarization method mentioned in a review paper by Gupta (2010) <doi:10.4304/jetwi.2.3.258-268>.
Imports: R6, mlapi, stringr, tidyr, tokenizers, text2vec, Matrix.utils,