Execute the self-controlled case series (SCCS) design using observational data in the OMOP Common Data Model. Extracts all necessary data from the database and transforms it to the format required for SCCS. Age and season can be modeled using splines assuming constant hazard within calendar months. Event-dependent censoring of the observation period can be corrected for. Many exposures can be included at once (MSCCS), with regularization on all coefficients except for the exposure of interest. Includes diagnostics for all major assumptions of the SCCS.

It computes full conformal, split conformal and multi-split conformal prediction regions when the response variable is multivariate (i.e. dimension is greater than one). Moreover, the package also contains plot functions to visualize the output of the full and split conformal functions. To guarantee consistency, the package structure mimics the univariate package conformalInference by Ryan Tibshirani. See Lei, Gâ sell, Rinaldo, Tibshirani, & Wasserman (2018) <doi:10.1080/01621459.2017.1307116> for full and split conformal prediction in regression, and Barber, Candès, Ramdas, & Tibshirani (2023) <doi:10.1214/23-AOS2276> for extensions beyond exchangeability.

The tidySummarizedExperiment package provides a set of tools for creating and manipulating tidy data representations of SummarizedExperiment objects. SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis.

Fits Gaussian Mixtures by applying evolution. As fitness function a mixture of the chi square test for distributions and a novel measure for approximating the common area under curves between multiple Gaussians is used. The package presents an alternative to the commonly used Likelihood Maximization as is used in Expectation Maximization. The algorithm and applications of this package are published under: Lerch, F., Ultsch, A., Lotsch, J. (2020) <doi:10.1038/s41598-020-57432-w>. The evolution is based on the GA package: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04> while the Gaussian Mixture Logic stems from AdaptGauss': Ultsch, A, et al. (2015) <doi:10.3390/ijms161025897>.

Computes experimental designs for a two-arm experiment with covariates via a number of methods: (0) complete randomization and randomization with forced-balance, (1) Greedily optimizing a balance objective function via pairwise switching. This optimization provides lower variance for the treatment effect estimator (and higher power) while preserving a design that is close to complete randomization. We return all iterations of the designs for use in a permutation test, (2) The second is via numerical optimization (via gurobi which must be installed, see <https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>) a la Bertsimas and Kallus, (3) rerandomization, (4) Karp's method for one covariate, (5) exhaustive enumeration to find the optimal solution (only for small sample sizes), (6) Binary pair matching using the nbpMatching library, (7) Binary pair matching plus design number (1) to further optimize balance, (8) Binary pair matching plus design number (3) to further optimize balance, (9) Hadamard designs, (10) Simultaneous Multiple Kernels. In (1-9) we allow for three objective functions: Mahalanobis distance, Sum of absolute differences standardized and Kernel distances via the kernlab library. This package is the result of a stream of research that can be found in Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) <arXiv:1612.02315>, Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization" (2021) <arXiv:2012.03330>.

Affymetrix rta10 annotation data (chip rta10transcriptcluster) assembled using data from public repositories.

This package provides an implementation of the RDF4J Rio API, which reads and writes TriG.

This package provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.

Datasets to support COPDSexaulDimorphism Package.

Documentation at https://melpa.org/#/realgud-old-debuggers

Documentation at https://melpa.org/#/clojure-essential-ref

Documentation at https://melpa.org/#/friendly-remote-shell

Documentation at https://melpa.org/#/dired-rsync-transient

Documentation at https://melpa.org/#/discover-js2-refactor

Documentation at https://melpa.org/#/discover-clj-refactor

Pabot is a parallel executor for Robot Framework tests.

Rack::Mount supports Rack's X-Cascade convention to continue trying routes if the response returns pass. This allows multiple routes to be nested or stacked on top of each other.

Color ANSI codes in the REPL of SLIME

This package provides primitives for generating random values.

Documentation at https://melpa.org/#/slime-repl-ansi-color

Parses templates for render calls and uses them to precompile

This application is a terminal-based sequence editor for git interactive rebase.

Affymetrix hta20 annotation data (chip hta20transcriptcluster) assembled using data from public repositories.

Affymetrix mta10 annotation data (chip mta10transcriptcluster) assembled using data from public repositories.