Retrieve Information from ‘Google Knowledge Graph’ API (GoogleKnowledgeGraphR)
Allows you to retrieve information from the ‘Google Knowledge Graph’ API <https://…/knowledge.html> and process it in R in various forms. The ‘Knowledge Graph Search’ API lets you find entities in the ‘Google Knowledge Graph’. The API uses standard ‘schema.org’ types and is compliant with the ‘JSON-LD’ specification.

Fast Region-Based Association Tests on Summary Statistics (sumFREGAT)
An adaptation of classical region/gene-based association analysis techniques that uses summary statistics (P values and effect sizes) and correlations between genetic variants as input. It is a tool to perform the most common and efficient gene-based tests on the results of genome-wide association (meta-)analyses without having the original genotypes and phenotypes at hand.

Monotonic Association on Zero-Inflated Data (mazeinda)
Methods for calculating and testing the significance of pairwise monotonic association from and based on the work of Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one or multiple sets can be performed in parallel thanks to the packages ‘foreach’ and ‘doMC’.

Computing Envelope Estimators (Renvlp)
Provides a general routine, envMU(), which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU() does not presume a model. This package implements response envelopes (env()), partial response envelopes (penv()), envelopes in the predictor space (xenv()), heteroscedastic envelopes (henv()), simultaneous envelopes (stenv()), scaled response envelopes (senv()), scaled envelopes in the predictor space (sxenv()), groupwise envelopes (genv()), weighted envelopes (weighted.env(), weighted.penv() and weighted.xenv()), envelopes in logistic regression (logit.env()), and envelopes in Poisson regression (pois.env()). For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.

Model Based Random Forest Analysis (mobForest)
Functions to implements random forest method for model based recursive partitioning. The mob() function, developed by Zeileis et al. (2008), within ‘party’ package, is modified to construct model-based decision trees based on random forests methodology. The main input function mobforest.analysis() takes all input parameters to construct trees, compute out-of-bag errors, predictions, and overall accuracy of forest. The algorithm performs parallel computation using cluster functions within ‘parallel’ package.

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