Sample size and confidence interval calculations in reversible catalytic models, with applications in malaria research. Further details can be found in the paper by Sepúlveda and Drakeley (2015, <doi:10.1186/s12936-015-0661-z>).

R functions for the computation of the truncated maximum likelihood and the robust accelerated failure time regression for gaussian and log-Weibull case.

Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the PACTA methodology (Paris Agreement Capital Transition Assessment; <https://www.transitionmonitor.com/>). Financial institutions use PACTA to study how their capital allocation decisions align with climate change mitigation goals.

Fetches NCBI data (RefSeq <https://www.ncbi.nlm.nih.gov/refseq/> database) and provides an environment to extract information at the level of gene, mRNA or protein accessions.

This package performs goodness of fits tests for both high and low-dimensional linear models. It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity. In addition one can test the significance of potentially large groups of variables, and also produce p-values for the significance of individual variables in high-dimensional linear regression.

The JSON format is ubiquitous for data interchange, and the simdjson library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel SIMD instruction manages to parse these files as faster than disk speed. See the <doi:10.48550/arXiv.1902.08318> paper for more details about simdjson'. This package parses JSON from string, file, or remote URLs under a variety of settings.

For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) <doi:10.3102/10769986024001070>.

Computes a variety of statistics for relational event models. Relational event models enable researchers to investigate both exogenous and endogenous factors influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models.

Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the BIC and the BDeu score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.

This package provides a collection of functions to simulate dice rolls and the like. In particular, experiments and exercises can be performed looking at combinations and permutations of values in dice rolls and coin flips, together with the corresponding frequencies of occurrences. When applying each function, the user has to input the number of times (rolls, flips) to toss the dice. Needless to say, the more the tosses, the more the frequencies approximate the actual probabilities. Moreover, the package provides functions to generate non-transitive sets of dice (like Efron's) and to check whether a given set of dice is non-transitive with given probability.

Jade is a high performance template engine heavily influenced by Haml and implemented with JavaScript for node and browsers.

Interface to JDemetra+ 3.x (<https://github.com/jdemetra>) time series analysis software. It provides a variety of methods for temporal disaggregation & interpolation, benchmarking, reconciliation and calendarization. It incorporates statistical methods described in the latest European Statistical System (ESS) guidelines on temporal disaggregation, benchmarking, and reconciliation (2018 edition). The package implements highly efficient algorithms for fast and reliable computation.

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.

The Public Trading API <https://public.com/api/docs> allows clients to access their brokerage accounts, request market data, and place stock/etf/option orders.

The algorithm provided in this package generates perfect sample for unimodal or multimodal posteriors. Read Once Coupling From The Past, with Metropolis-Multishift is used to generate a perfect sample for a given posterior density based on the two extreme starting paths, minimum and maximum of the most interest range of the posterior. It uses the monotone random operation of multishift coupler which allows to sandwich all of the state space in one point. It means both Markov Chains starting from the maximum and minimum will be coalesced. The generated sample is independent from the starting points. It is useful for mixture distributions too. The output of this function is a real value as an exact draw from the posterior distribution.

EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles.

Read and write labelled sparse matrices in text format as used by software such as SVMLight', LibSVM', ThunderSVM', LibFM', xLearn', XGBoost', LightGBM', and others. Supports labelled data for regression, classification (binary, multi-class, multi-label), and ranking (with qid field), and can handle header metadata and comments in files.

Minimal and lightweight configuration tool that provides basic support for YAML configuration files without requiring additional package dependencies. It offers a simple method for loading and parsing configuration settings, making it ideal for quick prototypes and lightweight projects.

Perform robust estimation and inference in platform trials and other master protocol trials. Yuhan Qian, Yifan Yi, Jun Shao, Yanyao Yi, Gregory Levin, Nicole Mayer-Hamblett, Patrick J. Heagerty, Ting Ye (2025) <doi:10.48550/arXiv.2411.12944>.

Accurate prediction of subject recruitment for Randomized Clinical Trials (RCT) remains an ongoing challenge. Many previous prediction models rely on parametric assumptions. We present functions for non-parametric RCT recruitment prediction under several scenarios.

In order to facilitate parsing of http requests and creating appropriate responses this package provides two classes to handle a lot of the housekeeping involved in working with http exchanges. The infrastructure builds upon the rook specification and is thus well suited to be combined with httpuv based web servers.

Shiny-based interactive gadgets of radial visualization methods and extensions thereof.

The provided package implements the statistical tests for the functional repeated measures analysis problem (Kurylo and Smaga, 2023, <arXiv:2306.03883>). These procedures enable us to verify the overall hypothesis regarding equality, as well as hypotheses for pairwise comparisons (i.e., post hoc analysis) of mean functions corresponding to repeated experiments.

Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) <doi:10.1016/S1574-0099(05)02017-6> for DCCV.