This is a package for saving SingleCellExperiment into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This package provides tools to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome.

This package improves and replaces the GNU Emacs commands that interactively evaluate Emacs Lisp expressions. The new commands replace standard key bindings and are all prefixed with rsw-elisp-. They work the same way as the old commands when called non-interactively; only the interactive behavior should be different.

Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.

Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) <doi:10.1016/j.beth.2010.08.006> was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.

Overture Maps offers free and open geospatial map data sourced from various providers and standardized to a common schema. This tool allows you to download Overture Maps data for a specific region of interest and convert it to several different file formats. For more information, visit <https://overturemaps.org/download/>.

This package adds the ability to run tests by filtering the test tree based on the result of a previous test run. You can use this to run only those tests that failed in the last run, or to only run the tests that have been added since previous test run.

This package provides a GUI to correct measurement bias in DNA methylation analyses. The BiasCorrector package just wraps the functions implemented in the R package rBiasCorrection into a shiny web application in order to make them more easily accessible. Publication: Kapsner et al. (2021) <doi:10.1002/ijc.33681>.

Code for fitting and assessing models for the growth of trees. In particular for the Bayesian neighborhood competition linear regression model of Allen (2020): methods for model fitting and generating fitted/predicted values, evaluating the effect of competitor species identity using permutation tests, and evaluating model performance using spatial cross-validation.

This package performs Granger causality tests on pairs of time series to determine causal relationships. Uses Vector Autoregressive (VAR) models to test whether one time series helps predict another beyond what the series own past values provide. Returns structured results including p-values, test statistics, and causality conclusions for both directions.

Identify the optimal timing for new treatment initiation during multiple state disease transition, including multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) <doi:10.18637/jss.v038.i07>.

Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the gurobi software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.

Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.

This package provides functions to compute standardized differences for numeric, binary, and categorical variables on Apache Spark DataFrames using sparklyr'. The implementation mirrors the methods used in the stddiff package but operates on distributed data. See Zhicheng Du, Yuantao Hao (2022) <doi:10.32614/CRAN.package.stddiff> for reference.

Fit additive mixed meta-analysis (AMMA) models, extending the mixmeta package <https://cran.r-project.org/package=mixmeta> to allow for spline-based meta-regression. Functions combine features of mgcv <https://cran.r-project.org/package=mgcv> for building spline components and mixmeta for estimating general mixed-effects meta-analysis models.

When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting. The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) <doi:10.1080/00031305.2022.2063944>.

Allows to generate on-demand or by batch, any R documentation file, whatever is kind, data, function, class or package. It populates documentation sections, either automatically or by considering your input. Input code could be standard R code or offensive programming code. Documentation content completeness depends on the type of code you use. With offensive programming code, expect generated documentation to be fully completed, from a format and content point of view. With some standard R code, you will have to activate post processing to fill-in any section that requires complements. Produced manual page validity is automatically tested against R documentation compliance rules. Documentation language proficiency, wording style, and phrasal adjustments remains your job.

The test-queue module is a parallel test runner, built using a centralized queue to ensure optimal distribution of tests between workers. It is specifically optimized for Continuous Integration (CI) environments: build statistics from each run are stored locally and used to sort the queue at the beginning of the next run.

This package provides an easy to use command. It takes an URL of the Research Organization Registry (ROR) as argument and creates a ROR symbol which links to the given URL---very similar to the orcidlink package from which it is derived. The symbol itself always fits with the chosen font size.

The package Fletcher2013a contains time-course gene expression data from MCF-7 cells treated under different experimental systems in order to perturb FGFR2 signalling. The data comes from Fletcher et al. (Nature Comms 4:2464, 2013) where further details about the background and the experimental design of the study can be found.

omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA).

Implement maximum likelihood estimation for Poisson generalized linear models with grouped and right-censored count data. Intended to be used for analyzing grouped and right-censored data, which is widely applied in many branches of social sciences. The algorithm implemented is described in Fu et al., (2021) <doi:10.1111/rssa.12678>.

This package implements the sample size methods for hierarchical 2x2 factorial trials under two choices of effect estimands and a series of hypothesis tests proposed in "Sample size calculation in hierarchical 2x2 factorial trials with unequal cluster sizes" (under review), and provides the table and plot generators for the sample size estimations.

Fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools. The kernel smoothing is based on methods described in Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.