Title: The Topic SCORE Algorithm to Fit Topic Models
Authors@R: c(person("Minzhe", "Wang", role = c("aut", "cre"),
Description: Provides implementation of the "Topic SCORE" algorithm that is
proposed by Tracy Ke and Minzhe Wang. The singular value decomposition
step is optimized through the usage of svds() function in 'RSpectra'
package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise
error measure in the word-topic matrix A, and an algorithm for
recovering the topic-document matrix W given A and D based on
The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) <arXiv:1704.07016>.
Imports: utils, stats, graphics, RSpectra, combinat, quadprog, methods,
Packaged: 2019-06-04 15:32:17 UTC; greenlink

Description: Quantifies how balanced a phylogenetic tree is, using the Total Cophenetic Index
- per A. Mir, F. Rossello, L. A. Rotger (2013), A new balance index for phylogenetic trees.

Title: Total Cophenetic Index: An R Package to Quantify Tree Balance
Description: For a given phylogenetic tree, calculates the Total Cophenetic Index.
Reference - A. Mir, F. Rossello, L. A. Rotger, A new balance index for phylogenetic trees.

Title: An R Package to Quantify Tree Balance
Reference: A. Mir, F. Rossello, L. A. Rotger (2013).
A new balance index for phylogenetic trees.
Suggests: ape, CollessLike, knitr, testthat

Title: Quantify the Balance of Phylogenetic Trees
Description: Measures the degree of balance for a given phylogenetic tree by

Authors@R: person("Tobias", "Wolfanger", email = "tobias.wolfanger@gmx.de", role = c("aut","cre"))
Description: Contains two functions related to sports competitions. One to create league tables and one to create a match schedule.

Title: Calculates Safety Stopping Boundaries for a Single-Arm Trial
Authors@R: c(person("NCT Trial Center, Heidelberg",
Author: NCT Trial Center, Heidelberg [aut],
Description: Computation of stopping boundaries for a single-arm trial using a
Bayesian criterion; i.e., for each m<=n (n= total patient number of the
trial) the smallest number of observed toxicities is calculated
leading to the termination of the trial/accrual according to the specified
criteria. The probabilities of stopping the trial/accrual at and up until
(resp.) the m-th patient (m<=n) is also calculated. This design is more
conservative than the frequentist approach (using Clopper Pearson CIs)
which might be preferred as it concerns safety.See also Aamot et.al.(2010)
"Continuous monitoring of toxicity in clinical trials - simulating the risk