Cross-validation for linear discriminate analysis
This is the gui interface to calculate cross-validation of the principle component and discriminate analysis

This is the gui interface to calculate cross-validation of
the principle component and discriminate analysis

Fit Latent Dirichlet Allocation Models using Stochastic
Variational Inference
person("Nicholas", "Erskine", email = "nicholas.erskine95@gmail.com", role = c("aut", "cre"))
Fits Latent Dirichlet Allocation topic models to text data using the stochastic variational inference algorithm described in Hoffman et. al. (2013) <arXiv:1206.7051v3>. This method is more efficient than the original batch variational inference algorithm for LDA, and allows users to fit LDA models with more topics and to larger text corpora than would be feasible using that older method.
Rcpp, reshape2, tm (>= 0.6), methods, Rdpack

Prototype of Multiple Latent Dirichlet Allocation Runs
person("Jonas", "Rieger", email="jonas.rieger@tu-dortmund.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0007-4478"))
Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.
batchtools (>= 0.9.11), future, parallelMap, RColorBrewer (>=
1.1-2), testthat, tosca

batchtools (>= 0.9.11), checkmate (>= 1.8.5), colorspace (>=
1.4-1), data.table (>= 1.11.2), dendextend, fs (>= 1.2.0),
future, lda (>= 1.4.2), parallelMap, progress (>= 1.1.1),
stats, utils
RColorBrewer (>= 1.1-2), testthat, tosca

covr, RColorBrewer (>= 1.1-2), testthat, tosca

Multivariate Condition Matching by Backwards Elimination Using
Linear Discriminant Analysis
c(person("Kyle", "Gorman", email = "kylebgorman@gmail.com", role = c("aut", "cre")), person("Geza", "Kiss", email = "kiss@ohsu.edu", role = c("ctb")))
Performs group matching by backward elimination using linear discriminant analysis.
RUnit, data.table, foreach, kSamples, MASS, entropy,
iterators, iterpc