ISAAC (Indirection, Shift, Accumulate, Add, and Count) is a fast pseudo-random number generator. It is suitable for applications where a significant amount of random data needs to be produced quickly, such as solving using the Monte Carlo method or for games. The results are uniformly distributed, unbiased, and unpredictable unless you know the seed.

This package implements the same interface as Math::Random::ISAAC.

The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage.

This package implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. Also provides the finite-sample RMCD method discussed in the paper, as well as the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> and Green and Martin (2017) <https://christopherggreen.github.io/papers/hr05_extension.pdf>. See also Chapter 2 of Green (2017) <https://digital.lib.washington.edu/researchworks/handle/1773/40304>.

This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest.

The development of post-processing functionality for simulated snow profiles by the snow and avalanche community is often done in python'. This package aims to make some of these tools accessible to R users. Currently integrated modules contain functions to calculate dry snow layer instabilities in support of avalache hazard assessments following the publications of Richter, Schweizer, Rotach, and Van Herwijnen (2019) <doi:10.5194/tc-13-3353-2019>, and Mayer, Van Herwijnen, Techel, and Schweizer (2022) <doi:10.5194/tc-2022-34>.

This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed. Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices. Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments" Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) <arXiv:1905.03337>.

Automatically builds 12 classification models from data. The package returns 26 plots, 5 tables and a summary report. The package automatically builds six individual classification models, including error (RMSE) and predictions. That data is used to create an ensemble, which is then modeled using six methods. The process is repeated as many times as the user requests. The mean of the results are presented in a summary table. The package returns the confusion matrices for all 12 models, tables of the correlation of the numeric data, the results of the variance inflation process, the head of the ensemble and the head of the data frame.

Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al., 2014 <DOI:10.1111/ddi.12160>). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 <DOI:10.1017/S0376892997000088>). Users of this package should be familiar with Maxent niche modelling.

This package provides tools for sampling from a conditional copula density decomposed via Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a sampling are those which sample as first the conditioning variables (when following the sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine (based on information criteria) among those which allow for such a conditional sampling. The package includes a function to compare scatterplot matrices and pair-dependencies of two multivariate datasets.

Ember for Rails 3.1+

Roberts2005Annotation Annotation Data (Roberts2005Annotation) assembled using data from public repositories.

This package provides an API for parsers and writers of various RDF formats.

Package retry provides a simple, stateless, functional mechanism to perform actions repetitively until successful.

U-Boot is a bootloader used mostly for ARM boards. It also initializes the boards (RAM etc).

Agilent Chips that use Agilent design number 028282 annotation data (chip RnAgilentDesign028282) assembled using data from public repositories.

This package was automatically created by package AnnotationForge version 1.7.17. The exon-level probeset genome location was retrieved from Netaffx using AffyCompatible.

Collection of somatic cancer alteration datasets.

Documentation at https://melpa.org/#/ido-completing-read+

Documentation at https://melpa.org/#/typewriter-roll-mode

Documentation at https://melpa.org/#/ruby-extra-highlight

Documentation at https://melpa.org/#/flycheck-falco-rules

Documentation at https://melpa.org/#/org-reverse-datetree

Documentation at https://melpa.org/#/sly-named-readtables

Documentation at https://melpa.org/#/bug-reference-github