This package provides a programmatic interface to web-services of YouTheria. YouTheria is an online database of mammalian trait data <http://www.utheria.org/>.

Aims to create a single isolated Miniconda and Python environment for reproducible pipeline scripts. The package provides utilities to run system command within the conda environment, making it easy to install, launch, manage, and stop Jupyter-lab'.

An R API Client for Valve's Dota2. RDota2 can be easily used to connect to the Steam API and retrieve data for Valve's popular video game Dota2. You can find out more about Dota2 at <http://store.steampowered.com/app/570/>.

Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.

Recursive lists in the form of R objects, JSON', and XML', for use in teaching and examples. Examples include color palettes, Game of Thrones characters, GitHub users and repositories, music collections, and entities from the Star Wars universe. Data from the gapminder package is also included, as a simple data frame and in nested and split forms.

This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) <doi:10.1097/EDE.0000000000001643>. It also accommodates the original SAS macro and PROC CAUSALMED procedure in SAS when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.

Unified object oriented interface for multiple independent streams of random numbers from different sources.

Simplified scenario testing and sensitivity analysis, redesigned to use packages future and furrr'. Provides functions for generating function argument sets using one-factor-at-a-time (OFAT) and (sampled) permutations.

This package provides data processing and summarization of data from FishNet2.net in text and graphical outputs. Allows efficient filtering of information and data cleaning.

Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.

Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.

R utilities for gff files, either general feature format (GFF3) or gene transfer format (GTF) formatted files. This package includes functions for producing summary stats, check for consistency and sorting errors, conversion from GTF to GFF3 format, file sorting, visualization and plotting of feature hierarchy, and exporting user defined feature subsets to SAF format. This tool was developed by the BioinfoGP core facility at CNB-CSIC.

This package provides a framework for estimating ensembles of meta-analytic, meta-regression, and multilevel models (assuming either presence or absence of the effect, heterogeneity, publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to combine the competing meta-analytic models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual components (e.g., effect vs. no effect; BartoÅ¡ et al., 2022, <doi:10.1002/jrsm.1594>; Maier, BartoÅ¡ & Wagenmakers, 2022, <doi:10.1037/met0000405>; BartoÅ¡ et al., 2025, <doi:10.1037/met0000737>). Users can define a wide range of prior distributions for the effect size, heterogeneity, publication bias (including selection models and PET-PEESE), and moderator components. The package provides convenient functions for summary, visualizations, and fit diagnostics.

This package provides methods for calculation and visualization of the Repertoire Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) <doi:10.1186/s12859-017-1556-5>.

Sundry discrete probability distributions and helper functions.

Measure single-storage water supply system performance using resilience, reliability, and vulnerability metrics; assess storage-yield-reliability relationships; determine no-fail storage with sequent peak analysis; optimize release decisions for water supply, hydropower, and multi-objective reservoirs using deterministic and stochastic dynamic programming; generate inflow replicates using parametric and non-parametric models; evaluate inflow persistence using the Hurst coefficient.

This package provides R functions to selectively rasterize components of grid output.

Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) <https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.

This package provides an R interface to the RCSB Protein Data Bank ('PDB') Search and Data APIs (<https://www.rcsb.org/>). Supports full-text, attribute, sequence, motif, structure, and chemical searches; retrieval of entry-, assembly-, polymer-entity-, and chemical-component-level metadata; and conversion of API responses into analysis-ready tables and typed R objects for reproducible structural bioinformatics workflows.

Slow Feature Analysis (SFA), ported to R based on matlab implementations of SFA: SFA toolkit 1.0 by Pietro Berkes and SFA toolkit 2.8 by Wolfgang Konen.

Computes the power resulting from completely randomized and rerandomized experiments with two groups. Furthermore, computes the sample size necessary to obtain a desired level of power for completely randomized and rerandomized experiments.

This package provides tools for generation of (life-history) traits and diversity maps on hexagonal or square grids. Valcu et al.(2012) <doi:10.1111/j.1466-8238.2011.00739.x>.

Test for effects of both individual factors and their interaction on replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.

Rcmdr interface to the sos package. The plug-in renders the sos searching functionality easily accessible via the Rcmdr menus. It also simplifies the task of performing multiple searches and subsequently obtaining the union or the intersection of the results.