Platform Design Info for The Manufacturer's Name Mu11KsubA.

Platform Design Info for The Manufacturer's Name Mu11KsubB.

This package provides a package containing an environment representing the Mu19KsubB.CDF file.

This package provides a package containing an environment representing the Mu11KsubA.CDF file.

This package provides a package containing an environment representing the Mu19KsubA.CDF file.

This package provides a package containing an environment representing the Mu11KsubB.CDF file.

This package provides a package containing an environment representing the Mu19KsubC.CDF file.

Affymetrix Affymetrix Mu11KsubB Array annotation data (chip mu11ksubb) assembled using data from public repositories.

Affymetrix Affymetrix Mu19KsubC Array annotation data (chip mu19ksubc) assembled using data from public repositories.

Affymetrix Affymetrix Mu19KsubB Array annotation data (chip mu19ksubb) assembled using data from public repositories.

Affymetrix Affymetrix Mu19KsubA Array annotation data (chip mu19ksuba) assembled using data from public repositories.

Affymetrix Affymetrix Mu11KsubA Array annotation data (chip mu11ksuba) assembled using data from public repositories.

Flexible implementation of a structural change point detection algorithm for multivariate time series. It authorizes inclusion of trends, exogenous variables, and break test on the intercept or on the full vector autoregression system. Bai, Lumsdaine, and Stock (1998) <doi:10.1111/1467-937X.00051>.

Exports two functions implementing multi-way clustering using the method suggested by Cameron, Gelbach, & Miller (2011) and cluster (or block) bootstrapping for estimating variance-covariance matrices. Normal one and two-way clustering matches the results of other common statistical packages. Missing values are handled transparently and rudimentary parallelization support is provided.

This package provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.

Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time.

This package provides a collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.

This package implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>. Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42â 56, <doi:10.1037/a0021150>) or to perform outcome-based strategy classification to select the decision strategy that provides the best account for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26â 40. <doi:10.1016/j.cogpsych.2017.05.003>).

Documentation at https://melpa.org/#/multitran

Documentation at https://melpa.org/#/multi-run

Documentation at https://melpa.org/#/evil-mu4e

Documentation at https://melpa.org/#/mutt-mode

This package provides a library implementing patterns that behave like regular expressions.

This package provides a package containing an environment representing the Mu6500subD.CDF file.