Assess LCâ MS system performance by visualizing instrument log files and monitoring raw quality control samples within a project.

Density discontinuity testing (a.k.a. manipulation testing) is commonly employed in regression discontinuity designs and other program evaluation settings to detect perfect self-selection (manipulation) around a cutoff where treatment/policy assignment changes. This package implements manipulation testing procedures using the local polynomial density estimators: rddensity() to construct test statistics and p-values given a prespecified cutoff, rdbwdensity() to perform data-driven bandwidth selection, and rdplotdensity() to construct density plots.

Display a randomly selected quote about Richard M. Stallman based on the collection in the GNU Octave function fact() which was aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site stallmanfacts.com (which is accessible only via <http://archive.org>).

R on FHIR is an easy to use wrapper around the HL7 FHIR REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official HL7 FHIR .NET API', also made by Firely.

This package provides methods for estimating online robust reduced-rank regression. The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) <doi:10.2307/2938278>. The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) <doi:10.1109/GlobalSIP.2017.8309093>. The description of the generic stochastic successive upper-bound minimisation method and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) <doi:10.1007/s10107-016-1021-7>.

The R Formatter formats R source code. It is very much based on formatR, but tries to improve it by heuristics. For example, spaces can be forced around the division operator "/".

This package provides a Tidy implementation of grouping sets', rollup and cube - extensions of the group_by clause that allow for computing multiple group_by clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" <https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup>.

This package provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling.

Create and manipulate hypergraph objects. This early version of rhype allows for the output of matrices associated with the hypergraphs themselves. It also uses these matrices to calculate hypergraph spectra and perform spectral comparison. Functionality coming soon includes calculation of hyperpaths and hypergraph centrality measures.

The minimal rrapply'-package contains a single function rrapply(), providing an extended implementation of R'-base rapply() by allowing to recursively apply a function to elements of a nested list based on a general condition function and including the possibility to prune or aggregate nested list elements from the result. In addition, special arguments can be supplied to access the name, location, parents and siblings in the nested list of the element under evaluation. The rrapply() function builds upon rapply()'s native C implementation and requires no other package dependencies.

Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.

Graphical visualization of the birds molt to facilitate the creation of molting graph for passerines having 9 (Rmolt(data,9)) or 10 primaries (Rmolt(data,10)), and also only for the 10 first primaries (Rmolt(data,"10_0")).

Efficient framework for ridge redundancy analysis (rrda), tailored for high-dimensional omics datasets where the number of predictors exceeds the number of samples. The method leverages Singular Value Decomposition (SVD) to avoid direct inversion of the covariance matrix, enhancing scalability and performance. It also introduces a memory-efficient storage strategy for coefficient matrices, enabling practical use in large-scale applications. The package supports cross-validation for selecting regularization parameters and reduced-rank dimensions, making it a robust and flexible tool for multivariate analysis in omics research. Please refer to our article (Yoshioka et al., 2025) for more details.

Toolbox with shiny applications for widely used psychometric methods. Those methods include following analysis: Item analysis, item response theory calibration, principal component analysis, confirmatory factor analysis - structural equation modeling, generating simulated data. References: Chalmers (2012, <doi:10.18637/jss.v048.i06>); Revelle (2022, <https://CRAN.R-project.org/package=psych Version = 2.2.9.>); Rosseel (2012, <doi:10.18637/jss.v048.i02>); Magis & Raiche (2012, <doi:10.18637/jss.v048.i08>); Magis & Barrada (2017, <doi:10.18637/jss.v076.c01>).

Bayes estimation of probit choice models in cross-sectional and panel settings. The package can analyze binary, multivariate, ordered, and ranked choices, as well as heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Gibbs sampling, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating heuristic. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method, see Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.

This package provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. This package implements response envelopes, partial response envelopes, envelopes in the predictor space, heteroscedastic envelopes, simultaneous envelopes, scaled response envelopes, scaled envelopes in the predictor space, groupwise envelopes, weighted envelopes, envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares, envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.

Autoencoding Random Forests ('RFAE') provide a method to autoencode mixed-type tabular data using Random Forests ('RF'), which involves projecting the data to a latent feature space of user-chosen dimensionality (usually a lower dimension), and then decoding the latent representations back into the input space. The encoding stage is useful for feature engineering and data visualisation tasks, akin to how principal component analysis ('PCA') is used, and the decoding stage is useful for compression and denoising tasks. At its core, RFAE is a post-processing pipeline on a trained random forest model. This means that it can accept any trained RF of ranger object type: RF', URF or ARF'. Because of this, it inherits Random Forests robust performance and capacity to seamlessly handle mixed-type tabular data. For more details, see Vu et al. (2025) <doi:10.48550/arXiv.2505.21441>.

Multivariate optimal allocation for different domains in one and two stages stratified sample design. R2BEAT extends the Neyman (1934) â Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethelâ s proposal (1989). R2BEAT develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of ReGenesees', that can be installed from <https://github.com/DiegoZardetto/ReGenesees>.

An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.

Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for randomForest results.

Estimate the percentage of seeds in a seedlot that contain stacks of genetically modified traits. Estimates are calculated using a multinomial group testing model with maximum likelihood estimation of the parameters.

Allow for easy-to-use testing or evaluating of linear equality and inequality restrictions about parameters and effects in (generalized) linear statistical models.

Rapid7 collects cybersecurity data and makes it available via their Open Data <http://opendata.rapid7.com> portal which has an API. Tools are provided to assist in querying for available data sets and downloading any data set authorized to a free, registered account.

Various functions to fit models for non-normal repeated measurements, such as Binary Random Effects Models with Two Levels of Nesting, Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models, Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, Discrete-time Hidden Markov Chain Models, Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain, generalized nonlinear autoregression models, multivariate Gaussian copula models, generalized non-linear mixed models with one random effect, generalized non-linear mixed models using h-likelihood for one random effect, Repeated Measurements Models for Counts with Frailty or Serial Dependence, Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence, Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square contingency tables, correlated negative binomial models with Kalman update. References include Lindsey's text books, JK Lindsey (2001) <isbn:10-0198508123> and JK Lindsey (1999) <isbn:10-0198505590>.