This package performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random.

This package provides ggplot2 extensions to construct glyph-maps for visualizing seasonality in spatiotemporal data. See the Journal of Statistical Software reference: Zhang, H. S., Cook, D., Laa, U., Langrené, N., & Menéndez, P. (2024) <doi:10.18637/jss.v110.i07>. The manuscript for this package is currently under preparation and can be found on GitHub at <https://github.com/maliny12/paper-sugarglider>.

MsgPack header files are provided for use by R packages, along with the ability to access, create and alter MsgPack objects directly from R. MsgPack is an efficient binary serialization format. It lets you exchange data among multiple languages like JSON but it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the msgpack-c implementation for C and C++(11) for use by R, particularly Rcpp'. The included msgpack-c headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files COPYRIGHTS and AUTHORS for a full list of copyright holders and contributors to msgpack-c'.

Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the Hit Rate is calculated), RR@K (reciprocal rank at k, from which the MRR or mean reciprocal rank is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation.

This package adds distinctive yet unobtrusive geometric patterns where solid color fills are normally used. Patterned figures look just as professional when viewed by colorblind readers or when printed in black and white. The dozen included patterns can be customized in terms of scale, rotation, color, fill, line type, and line width. It is compatible with the ggplot2 package as well as grid graphics.

This package helps you create plots of p-values using single SNP and/or haplotype data. Main features of the package include options to display a linkage disequilibrium (LD) plot and the ability to plot multiple datasets simultaneously. Plots can be created using global and/or individual haplotype p-values along with single SNP p-values. Images are created as either PDF/EPS files.

This package provides an XML-RPC client for Emacs capable of both synchronous and asynchronous method calls using the url package's async retrieval functionality. xml-rpc.el represents XML-RPC datatypes as Lisp values, automatically converting to and from the XML datastructures as needed, both for method parameters and return values, making using XML-RPC methods fairly transparent to the Lisp code.

Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare.

The crisprVerse is a modular ecosystem of R packages developed for the design and manipulation of CRISPR guide RNAs (gRNAs). All packages share a common language and design principles. This package is designed to make it easy to install and load the crisprVerse packages in a single step. To learn more about the crisprVerse, visit <https://www.github.com/crisprVerse>.

The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements.

Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, <doi:10.18637/jss.v094.i11>).

This package provides methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) <doi:10.1073/pnas.1703764114> for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood.

R Client for the Microsoft Cognitive Services Web Language Model REST API, including Break Into Words, Calculate Conditional Probability, Calculate Joint Probability, Generate Next Words, and List Available Models. A valid account MUST be registered at the Microsoft Cognitive Services website <https://www.microsoft.com/cognitive-services/> in order to obtain a (free) API key. Without an API key, this package will not work properly.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for ggplot2 plotting methods for nlmixr2 objects.

Extends the Heckman selection framework to panel data with individual random effects. The first stage models participation via a panel Probit specification, while the second stage can take a panel linear, Probit, Poisson, or Poisson log-normal form. Model details are provided in Bailey and Peng (2025) <doi:10.2139/ssrn.5475626> and Peng and Van den Bulte (2024) <doi:10.1287/mnsc.2019.01897>.

Synthesize numeric, categorical, mixed and time series data. Data circumstances including mixed (or zero-inflated) distributions and missing data patterns are reproduced in the synthetic data. A single parameter allows balancing between high-quality synthetic data that represents correlations of the original data and lower quality but more privacy safe synthetic data without correlations. Tuning can be done per variable or for the whole dataset.

Tests the hypothesis that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test. The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi10.1016/j.csda.2010.04.012.

An integrated suite of tools for creating, maintaining, and reusing FAIR (Findable, Accessible, Interoperable, Reusable) theories. Designed to support transparent and collaborative theory development, the package enables users to formalize theories, track changes with version control, assess pre-empirical coherence, and derive testable hypotheses. Aligning with open science principles and workflows, theorytools facilitates the systematic improvement of theoretical frameworks and enhances their discoverability and usability.

Set of functions designed to help in the analysis of TDP sensors. Features includes dates and time conversion, weather data interpolation, daily maximum of tension analysis and calculations required to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).

Adding some at-present missing functionality, or functions unlikely to be added to the base xpose package. This includes some diagnostic plots that have been missing in translation from xpose4', but also some useful features that truly extend the capabilities of what can be done with xpose'. These extensions include the concept of a set of xpose objects, and diagnostics for likelihood-based models.

This package defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning), quantitative aggregation functions (median polish, robust summarisation, etc.), missing data imputation, data normalisation (quantiles, vsn, etc.) as well as misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages.

This package generates graphics with embedded details from statistical tests. Statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous or categorical data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.

The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simonâ s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint.