Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. Comprehensive documentation and vignettes guide users through implementation and interpretation of results.

Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions for importing, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework as outlined in the Supplementary Material of Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.

It leverages the network-like architecture of scientific models together with software quality metrics to identify chains of function calls that are more prone to generating and propagating errors. It operates on tbl_graph objects representing call dependencies between functions (callers and callees) and computes risk scores for individual functions and for paths (sequences of function calls) based on cyclomatic complexity, in-degree and betweenness centrality. The package supports variance-based uncertainty and sensitivity analyses after Puy et al. (2022) <doi:10.18637/jss.v102.i05> to assess how risk scores change under alternative risk definitions.

English is the native language for only 5% of the World population. Also, only 17% of us can understand this text. Moreover, the Latin alphabet is the main one for merely 36% of the total. The early computer era, now a very long time ago, was dominated by the US. Due to the proliferation of the internet, smartphones, social media, and other technologies and communication platforms, this is no longer the case. This package replaces base R string functions (such as grep(), tolower(), sprintf(), and strptime()) with ones that fully support the Unicode standards related to natural language and date-time processing. It also fixes some long-standing inconsistencies, and introduces some new, useful features. Thanks to ICU (International Components for Unicode) and stringi', they are fast, reliable, and portable across different platforms.

This package performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.

This package provides functions to manipulate PDF files: fill out PDF forms; merge multiple PDF files into one; remove selected pages from a file; rename multiple files in a directory; rotate entire pdf document; rotate selected pages of a pdf file; Select pages from a file; splits single input PDF document into individual pages; splits single input PDF document into parts from given points.

Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks.

Fast computation of the required sample size or the achieved power, for GWAS studies with different types of covariate effects and different types of covariate-gene dependency structure. For the detailed description of the methodology, see Zhang (2022) "Power and Sample Size Computation for Genetic Association Studies of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.

Soil health assessment builds information to improve decision in soil management. It facilitates assessment of soil conditions for crop suitability [such as those given by FAO <https://www.fao.org/land-water/databases-and-software/crop-information/en/>], groundwater recharge, fertility, erosion, salinization [<doi:10.1002/ldr.4211>], carbon sequestration, irrigation potential, and status of soil resources.

Store persistent and synchronized data from shiny inputs within the browser. Refresh shiny applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using Dexie.js <https://dexie.org>, a minimal wrapper for IndexedDB'. Transfer browser link parameters to shiny input or output values. Store app visitor views, likes and followers.

It fits scale mixture of skew-normal linear mixed models using either an expectationâ maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence <doi:10.18637/jss.v115.i07>.

This package implements a generative model that uses a spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector. Such a model allows an EM algorithm, optimizing a type-II log-likelihood.

R language bindings for SolveBio's API. SolveBio is a biomedical knowledge hub that enables life science organizations to collect and harmonize the complex, disparate "multi-omic" data essential for today's R&D and BI needs.

This package provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like CellChat <https://github.com/jinworks/CellChat>, CellPhoneDB <https://www.cellphonedb.org/>, Cellinker <https://www.rna-society.org/cellinker/>, ICELLNET <https://github.com/soumelis-lab/ICELLNET>, and ConnectomeDB <https://humanconnectome.org/software/connectomedb/> to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales.

This package implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters.

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).

This package provides a computational framework for analyzing mutations in immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of antigen-driven selection pressure, mutational load quantification, building of somatic hypermutation (SHM) models, and model-dependent distance calculations. Also includes empirically derived models of SHM for both mice and humans. Citations: Gupta and Vander Heiden, et al (2015) <doi:10.1093/bioinformatics/btv359>, Yaari, et al (2012) <doi:10.1093/nar/gks457>, Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>, Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.

The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.

This package implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008), Journal of the Royal Statistical Society, Series B 71(3): 615-636.

This package provides a time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. SPORTSCausal (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.

This package performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).

This package provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. See Louviere et al. (2015) <doi:10.1017/CBO9781107337855> for details on best-worst scaling, and Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394> for the package.