This package provides functions for sample size estimation and simulation in clinical trials. Includes methods for selecting the best group using the Indifference-zone approach, as well as designs for non-inferiority, equivalence, and negative binomial models. For the sample size calculation for non-inferiority of vaccines, the approach is based on Fleming, Powers, and Huang (2021) <doi:10.1177/1740774520988244>. The Indifference-zone approach is based on Sobel and Huyett (1957) <doi:10.1002/j.1538-7305.1957.tb02411.x> and Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9).

The R implementation of TIGER. TIGER integrates random forest algorithm into an innovative ensemble learning architecture. Benefiting from this advanced architecture, TIGER is resilient to outliers, free from model tuning and less likely to be affected by specific hyperparameters. TIGER supports targeted and untargeted metabolomics data and is competent to perform both intra- and inter-batch technical variation removal. TIGER can also be used for cross-kit adjustment to ensure data obtained from different analytical assays can be effectively combined and compared. Reference: Han S. et al. (2022) <doi:10.1093/bib/bbab535>.

This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local false discovery rate (FDR) values. The q-value of a test measures the proportion of false positives incurred when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining.

SEMPLR computes transcription factor binding affinity scores for genomic positions and genetic variants. Scores are computed from SNP Effect Matrices (SEMs) produced by SEMpl. 223 pre-computed SEMs are included with the package or custom sets can be provided. Enrichment can be tested among sets of genomic positions to determine if transcription factor binding events occur more often than expected. Comparing binding affinity scores between alleles can reveal differences in transcription factor binding with genetic variation. This package also includes several visualization functions to view scores both on the motif and variant/position level.

This package implements a Fourier CUSUM test for the null hypothesis of cointegration in the presence of smooth structural change. The cointegrating regression is estimated by dynamic OLS with a Fourier pair at a fractional or integer frequency, and the residual CUSUM statistic of Xiao and Phillips (2002) <doi:10.1016/S0304-4076(01)00103-8> is scaled by a Bartlett long-run variance estimate. A sup-F pre-test decides whether the Fourier terms are needed, and bootstrap critical values that replicate the frequency search give correct size when a break is present.

Fits hidden Markov models to time-ordered sequences of curves, such as sample paths of stochastic processes or smoothed functional observations, without projecting the curves onto a finite basis. The emission functions are Onsager-Machlup functionals of Gaussian measures on function spaces, which allows for Brownian motion with drift, fractional Brownian motion, Ornstein-Uhlenbeck processes and non-parametric state means under a choice of Cameron-Martin norm. The Baum-Welch and Viterbi algorithms are implemented in C. Methods are described in Kashlak, Loliencar and Heo (2023) <https://jmlr.org/papers/v24/22-0685.html>.

This package provides a collection of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and the guilds sampling formula conditioned on guild size (Janzen et al. 2015).

This package implements regression models for bounded continuous data in the open interval (0,1) using the five-parameter Generalized Kumaraswamy distribution. Supports modeling all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of predictors through various link functions. Provides efficient maximum likelihood estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics, model comparison tools, and simulation methods. Particularly useful for analyzing proportions, rates, indices, and other bounded response data with complex distributional features not adequately captured by simpler models. Methods are described in Lopes and Bonat (2026) <doi:10.21105/joss.08991>.

This package provides Korean morphological analysis, part-of-speech (POS) tagging, noun extraction, Hangul (Korean script) conversion utilities, concordance search, mutual information statistics, and user dictionary management tools for Korean text research. The morphological analyzer backend is derived from the KoNLP package and the HanNanum analyzer, and is reimplemented in native C so that no Java runtime is required. Bundled dictionary and statistical resources allow analysis to work out of the box, and optional dictionaries can be loaded at runtime from the Sejong and NIADic packages when they are installed and loaded.

Estimation and diagnostic tools for instrumental variables designs with one endogenous treatment, implementing the guidelines in Lal, Lockhart, Xu, and Zu (2024) <doi:10.1017/pan.2024.2>. The package reports ordinary least squares and two-stage least squares estimates with analytic and bootstrapped confidence intervals, several first-stage F statistics including the effective F statistic, the Anderson-Rubin test and its confidence set, the tF procedure of Lee, McCrary, Moreira, and Porter (2022) <doi:10.1257/aer.20211063>, and local-to-zero sensitivity tests. It also supports fuzzy regression discontinuity designs.

This package provides user-friendly methods for handling missing data in regression modeling, including available-case linear regression, multiple imputation, random-forest imputation, and the Tower method. Implemented approaches include chained-equation imputation described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>, multiple imputation described by Honaker, King and Blackwell (2011) <doi:10.18637/jss.v045.i07>, random-forest imputation described by Stekhoven and Buehlmann (2012) <doi:10.1093/bioinformatics/btr597>, and the Tower method described by Matloff and Mohanty (2023) <https://CRAN.R-project.org/package=toweranNA>.

This package implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.

Estimation methods for phase-type distribution (PH) and Markovian arrival process (MAP) from empirical data (point and grouped data) and density function. The tool is based on the following researches: Okamura et al. (2009) <doi:10.1109/TNET.2008.2008750>, Okamura and Dohi (2009) <doi:10.1109/QEST.2009.28>, Okamura et al. (2011) <doi:10.1016/j.peva.2011.04.001>, Okamura et al. (2013) <doi:10.1002/asmb.1919>, Horvath and Okamura (2013) <doi:10.1007/978-3-642-40725-3_10>, Okamura and Dohi (2016) <doi:10.15807/jorsj.59.72>.

Calculates D-, Ds-, A-, I- and L-optimal designs, weighted combinations of these via a Compound criterion, and KL-optimal designs for model discrimination, for non-linear single- and multi-factor models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Multi-factor models use design variables x1, x2, â ¦ with a named-list design space; single-factor models remain backward compatible. Compares designs via their efficiency, augments any design with a controlled efficiency loss, and provides efficient rounding functions to convert approximate designs to exact ones.

Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.

Automatically fetch, transform and arrange subsets of multidimensional data sets (collections of files) stored in local and/or remote file systems or servers, using multicore capabilities where possible. This tool provides an interface to perceive a collection of data sets as a single large multidimensional data array, and enables the user to request for automatic retrieval, processing and arrangement of subsets of the large array. Wrapper functions to add support for custom file formats can be plugged in/out, making the tool suitable for any research field where large multidimensional data sets are involved.

This package provides a wrapper around Michel Scheffers's libassp (<https://libassp.sourceforge.net/>). The libassp (Advanced Speech Signal Processor) library aims at providing functionality for handling speech signal files in most common audio formats and for performing analyses common in phonetic science/speech science. This includes the calculation of formants, fundamental frequency, root mean square, auto correlation, a variety of spectral analyses, zero crossing rate, filtering etc. This wrapper provides R with a large subset of libassp's signal processing functions and provides them to the user in a (hopefully) user-friendly manner.

Fits multivariable bidirectional Mendelian randomization networks with Bayesian directed cyclic graphical models (reciprocal graphical models; Ni, Ji and Mueller (2018) <doi:10.1214/17-BA1087>), an earlier version of which is described in Sarkar and Ni (2025) <doi:10.1093/bioinformatics/btaf130>. The causal effects among the responses, which may form directed cycles, are estimated with spike-and-slab or threshold priors, and posterior probabilities of network motifs are provided. With a known instrument-response map the instrument effects obey the exclusion restrictions; with an unrestricted map they are selected by a spike-and-slab prior, which allows invalid instruments (horizontal pleiotropy). The input can be individual-level data, the second-moment matrices of one sample, or marginal instrument-response regressions, with optional measured covariates. The error covariance can be diagonal, inverse-Wishart, or a stochastic search structure learning prior (Wang (2015) <doi:10.1214/14-BA916>) whose inclusion indicators describe unmeasured confounding among the responses. Inference is by Markov chain Monte Carlo with exact Gibbs updates of the instrument effects and adaptive random-walk Metropolis updates of the causal effects (Roberts and Rosenthal (2009) <doi:10.1198/jcgs.2009.06134>); the fitting function reports diagnostics of weak instruments, weak identification and convergence.

Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.

This package provides a collection of functions and classes which serve as the foundation for packages such as PharmacoGx and RadioGx. It was created to abstract shared functionality to increase ease of maintainability and reduce code repetition in current and future Gx suite programs. Major features include a CoreSet class, from which RadioSet and PharmacoSet are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating AUC or SF are included.

The DHARMa package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as JAGS, STAN, or BUGS can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation.

Have you ever index sorted cells in a 96 or 384-well plate and then sequenced using Sanger sequencing? If so, you probably had some struggles to either check the electropherogram of each cell sequenced manually, or when you tried to identify which cell was sorted where after sequencing the plate. Scifer was developed to solve this issue by performing basic quality control of Sanger sequences and merging flow cytometry data from probed single-cell sorted B cells with sequencing data. scifer can export summary tables, fasta files, electropherograms for visual inspection, and generate reports.

This package implements the ASC-CFD (Assemblage Shift Characterization - Community Functional Dynamics) framework for decomposing functional community restructuring into positional (centroid displacement), dispersive (functional dispersion), and boundary (convex hull volume) components. Tests each component against two null models: a sampling null model (individuals or replicates reassigned between communities) and a stratified trait-permutation null model, with a single global test when many communities are compared. Functional entities (species with similar traits) help detect replacement among similar species, and species-level leverage identifies the taxa driving functional shifts. Supports both temporal (paired) and spatial (pairwise) comparisons.

Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433.