Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages.

Efficiently manage and process data from oTree experiments. Import oTree data and clean them by using functions that handle messy data, dropouts, and other problematic cases. Create IDs, calculate the time, transfer variables between app data frames, and delete sensitive information. Review your experimental data prior to running the experiment and automatically generate a detailed summary of the variables used in your oTree code. Information on oTree is found in Chen, D. L., Schonger, M., & Wickens, C. (2016) <doi:10.1016/j.jbef.2015.12.001>.

Generates a file, containing the main scientific references, prepared to be automatically inserted into an academic paper. The articles present in the list are chosen from the main references generated, by function principal_lister(), of the package bibliorefer'. The generated file contains the list of metadata of the principal references in BibTex format. Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>. Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>. Hamid DerviÅ . (2019) <doi:10.5530/jscires.8.3.32>.

It provides multiple functions that are useful for ecological research and teaching statistics to ecologists. It is based on data analysis courses offered at the Instituto de Ecologà a AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) <doi:10.1111/gcb.17282>, Manrique-Ascencio et al (2024) <doi:10.1111/plb.13683>, Ruiz-Guerra et al(2017) <doi:10.17129/botsci.812>, Juarez-Fragoso et al (2024) <doi:10.1007/s10980-024-01809-z>, Papaqui-Bello et al (2024) <doi:10.13102/sociobiology.v71i2.10503>.

This is designed for use with an arbitrary set of equations with an arbitrary set of unknowns. The user selects "fixed" values for enough unknowns to leave as many variables as there are equations, which in most cases means the system is properly defined and a unique solution exists. The function, the fixed values and initial values for the remaining unknowns are fed to a nonlinear backsolver. The original version of "TK!Solver" , now a product of Universal Technical Systems (<https://www.uts.com>) was the inspiration for this function.

This package provides a number of functions to facilitate the handling and production of reports using time series data. The package was developed to be understandable for beginners, so some functions aim to transform processes that would be complex into functions with a few lines. The main advantage of using the metools package is the ease of producing reports and working with time series using a few lines of code, so the code is clean and easy to understand/maintain. Learn more about the metools at <https://metoolsr.wordpress.com>.

Model based clustering using the multivariate multiple Scaled t (MST) and multivariate multiple scaled contaminated normal (MSCN) distributions. The MST is an extension of the multivariate Student-t distribution to include flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a heavy-tailed generalization of the multivariate normal (MN) distribution to model elliptical contoured scatters in the presence of mild outliers (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.

This package provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks.

Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series. Citation: Louca, Stilianos and Doebeli, Michael (2015) <doi:10.1890/14-0126.1>.

Inspired by space-time regressions often performed to assess the expansion of the Neolithic from the Near East to Europe (Pinhasi et al. 2005 <doi:10.1371/journal.pbio.0030410>). Test for significant correlations between the (earliest) radiocarbon dates of archaeological sites and their respective distances from a hypothetical center of origin. Both ordinary least squares (OLS) and reduced major axis (RMA) methods are supported (Russell et al. 2014 <doi:10.1371/journal.pone.0087854>). It is also possible to iterate over many sites to identify the most likely origin.

Spreadsheets silently turn entries such as 30.3 into dates, so the imported data carry date serials instead of the numbers that were typed. Reading the workbook directly recovers those numbers without guesswork: an xlsx file states its own date system, and records which cells are formatted as dates and in which field order, so the values to repair are identified from the file rather than inferred from their magnitude. Functions are also provided for data already imported, where that evidence is no longer available, using conservative and configurable detection.

biomaRt provides an interface to a growing collection of databases implementing the http://www.biomart.org. The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. Examples of BioMart databases are Ensembl, COSMIC, Uniprot, HGNC, Gramene, Wormbase and dbSNP mapped to Ensembl. These major databases give biomaRt users direct access to a diverse set of data and enable a wide range of powerful online queries from gene annotation to database mining.

This package provides a pipeline toolkit for statistics and data science in R; the targets package brings function-oriented programming to Make-like declarative pipelines. It orchestrates a pipeline as a graph of dependencies, skips steps that are already up to date, runs the necessary computation with optional parallel workers, abstracts files as R objects, and provides tangible evidence that the results are reproducible given the underlying code and data. The methodology in this package borrows from GNU Make (2015, ISBN:978-9881443519) and drake (2018, <doi:10.21105/joss.00550>).

Ratpoison is a simple window manager with no fat library dependencies, no fancy graphics, no window decorations, and no rodent dependence. It is largely modelled after GNU Screen which has done wonders in the virtual terminal market.

The screen can be split into non-overlapping frames. All windows are kept maximized inside their frames to take full advantage of your precious screen real estate.

All interaction with the window manager is done through keystrokes. Ratpoison has a prefix map to minimize the key clobbering that cripples Emacs and other quality pieces of software.

Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022).

PathMED is a collection of tools to facilitate precision medicine studies with omics data (e.g. transcriptomics). Among its funcionalities, genesets scores for individual samples may be calculated with several methods. These scores may be used to train machine learning models and to predict clinical features on new data. For this, several machine learning methods are evaluated in order to select the best method based on internal validation and to tune the hyperparameters. Performance metrics and a ready-to-use model to predict the outcomes for new patients are returned.

zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging.

Automated psychological scale development and structural validation using large language models (LLMs) and network psychometric methods. Implements the AI-GENIE framework (Automatic Item Generation and Validation via Network-Integrated Evaluation) to generate candidate items, compute embedding representations, and estimate dimensional structure using Exploratory Graph Analysis (EGA). Item quality is evaluated using Unique Variable Analysis to identify redundant items and Bootstrap EGA to assess item and dimension stability. Supports both fully automated item generation and analysis of user-provided item sets, facilitating efficient, theory-informed measurement development prior to empirical data collection.

This package provides functions to simulate data sets from hierarchical ecological models, including all the simulations described in the two volume publication Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS by Marc Kéry and Andy Royle: volume 1 (2016, ISBN: 978-0-12-801378-6) and volume 2 (2021, ISBN: 978-0-12-809585-0), <https://www.mbr-pwrc.usgs.gov/pubanalysis/keryroylebook/>. It also has all the utility functions and data sets needed to replicate the analyses shown in the books.

Bayesian kernel machine regression (from the bkmr package) is a Bayesian semi-parametric generalized linear model approach under identity and probit links. There are a number of functions in this package that extend Bayesian kernel machine regression fits to allow multiple-chain inference and diagnostics, which leverage functions from the future', rstan', and coda packages. Reference: Bobb, J. F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. ; <doi:10.1186/s12940-018-0413-y>.

Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation.

This package implements a modern, unified estimation strategy for common mediation estimands (natural effects, organic effects, interventional effects, and recanting twins) in combination with modified treatment policies as described in Liu, Williams, Rudolph, and DÃ az (2024) <doi:10.48550/arXiv.2408.14620>. Estimation makes use of recent advancements in Riesz-learning to estimate a set of required nuisance parameters with deep learning. The result is the capability to estimate mediation effects with binary, categorical, continuous, or multivariate exposures with high-dimensional mediators and mediator-outcome confounders using machine learning.

Estimation of distributed lag models (DLMs) based on a Bayesian additive regression trees framework. Includes several extensions of DLMs: treed DLMs and distributed lag mixture models (Mork and Wilson, 2023) <doi:10.1111/biom.13568>; treed distributed lag nonlinear models (Mork and Wilson, 2022) <doi:10.1093/biostatistics/kxaa051>; heterogeneous DLMs (Mork, et. al., 2024) <doi:10.1080/01621459.2023.2258595>; monotone DLMs (Mork and Wilson, 2024) <doi:10.1214/23-BA1412>. The package also includes visualization tools and a shiny interface to check model convergence and to help interpret results.

Connect to Elasticsearch and OpenSearch', NoSQL databases built on the Java Virtual Machine and using the Apache Lucene library. Interacts with the Elasticsearch HTTP API (<https://www.elastic.co/elasticsearch/>) and the OpenSearch HTTP API (<https://opensearch.org/>). Includes functions for setting connection details to Elasticsearch and OpenSearch instances, loading bulk data, searching for documents with both HTTP query variables and JSON based body requests. In addition, elastic provides functions for interacting with APIs for indices', documents, nodes, clusters, an interface to the cat API, and more.