The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017)<DOI:10.1186/s12859-016-1440-8>.

The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.

Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.

This package provides a collection of methods to estimate parameters of different tempered stable distributions (TSD). Currently, there are seven different tempered stable distributions to choose from: Tempered stable subordinator distribution, classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid decreasing TSD, and Kim-Rachev TSD. The package also provides functions to compute density and probability functions and tools to run Monte Carlo simulations. This package has already been used for the estimation of tempered stable distributions (Massing (2023) <arXiv:2303.07060>). The following references form the theoretical background for various functions in this package. References for each function are explicitly listed in its documentation: Bianchi et al. (2010) <doi:10.1007/978-88-470-1481-7_4> Bianchi et al. (2011) <doi:10.1137/S0040585X97984632> Carrasco (2017) <doi:10.1017/S0266466616000025> Feuerverger (1981) <doi:10.1111/j.2517-6161.1981.tb01143.x> Hansen et al. (1996) <doi:10.1080/07350015.1996.10524656> Hansen (1982) <doi:10.2307/1912775> Hofert (2011) <doi:10.1145/2043635.2043638> Kawai & Masuda (2011) <doi:10.1016/j.cam.2010.12.014> Kim et al. (2008) <doi:10.1016/j.jbankfin.2007.11.004> Kim et al. (2009) <doi:10.1007/978-3-7908-2050-8_5> Kim et al. (2010) <doi:10.1016/j.jbankfin.2010.01.015> Kuechler & Tappe (2013) <doi:10.1016/j.spa.2013.06.012> Rachev et al. (2011) <doi:10.1002/9781118268070>.

Download TIGER/Line shapefiles from the United States Census Bureau (<https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html>) and load into R as sf objects.

Trauma Mortality prediction for ICD-9, ICD-10, and AIS lexicons in long or wide format based on Dr. Alan Cook's tmpm mortality model.

The ts objects in R are managed using a very specific date format (in the form c(2022, 9) for September 2022 or c(2021, 2) for the second quarter of 2021, depending on the frequency, for example). We focus solely on monthly and quarterly series to manage the dates of ts objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic stats functions for creating and managing time series (ts(), window()). Les objets ts en R sont gérés par un format de date très particulier (sous la forme c(2022, 9) pour septembre 2022 ou c(2021, 2) pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets ts. Lidée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. Cest un compromis entre simplicité, précision et utilisation des fonctions du package stats de création et de gestion des séries temporelles (ts(), window()).

This package provides functions to access the database of 217 data-frames with aggregate study-level characteristics (that may act as effect modifiers) extracted from published systematic reviews with network meta-analysis. The database shall only be used for developing and appraising the methodology to assess the transitivity assumption quantitatively.

Makes data wrangling with ID-related aspects more comfortable. Provides functions that make it easy to inspect various subject-generated ID codes (SGIC) for plausibility. Also helps with inspecting other common identifiers, ensuring that your data stays clean and reliable.

Implementation of two transportation problem algorithms. 1. North West Corner Method 2. Minimum Cost Method or Least cost method. For more technical details about the algorithms please refer below URLs. <http://www.universalteacherpublications.com/univ/ebooks/or/Ch5/nw.htm>. <http://personal.maths.surrey.ac.uk/st/J.F/chapter7.pdf>.

This package provides functions for density, cumulative density, quantile and simulation of Tukey g-and-h (1977) distributions. The quantile-based transformation (Hoaglin 1985 <doi:10.1002/9781118150702.ch11>) and its reverse transformation, as well as the letter-value based estimates (Hoaglin 1985), are also provided.

Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.

This package provides functions for attaching tags to R objects, searching for objects based on tags, and removing tags from objects. It also includes a function for removing all tags from an object, as well as a function for deleting all objects with a specific tag from the R environment. The package is useful for organizing and managing large collections of objects in R.

This package provides a tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. TransProR is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include limma (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), edgeR (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), DESeq2 (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.

This package implements a likelihood ratio test and two pairwise standardized mean difference tests for testing equality of means against tree ordered alternatives in one-way ANOVA. The null hypothesis assumes all group means are equal, while the alternative assumes the control mean is less than or equal to each treatment mean with at least one strict inequality. Inputs are a list of numeric vectors (groups) and a significance level; outputs include the test statistic, critical value, and decision. Methods described in "Testing Against Tree Ordered Alternatives in One-way ANOVA" <doi:10.48550/arXiv.2507.17229>.

There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010<doi:10.1080/15598608.2010.10412013>). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design.

Likelihood-based estimation of mixed-effects transformation models using the Template Model Builder ('TMB', Kristensen et al., 2016) <doi:10.18637/jss.v070.i05>. The technical details of transformation models are given in Hothorn et al. (2018) <doi:10.1111/sjos.12291>. Likelihood contributions of exact, randomly censored (left, right, interval) and truncated observations are supported. The random effects are assumed to be normally distributed on the scale of the transformation function, the marginal likelihood is evaluated using the Laplace approximation, and the gradients are calculated with automatic differentiation (Tamasi & Hothorn, 2021) <doi:10.32614/RJ-2021-075>. Penalized smooth shift terms can be defined using the mgcv notation. Additive mixed-effects transformation models are described in Tamasi (2025) <doi:10.18637/jss.v114.i11>.

This package provides tools for constructing and analyzing two-phase experimental designs under correlated error structures. Version 1.1.1 includes improved efficiency factor classification with tolerance control, updated plot visualizations, and improved clarity of the results. The conceptual framework and the term two-phase were introduced by McIntyre (1955) <doi:10.2307/3001770>).

The tdROC package facilitates the estimation of time-dependent ROC (Receiver Operating Characteristic) curves and the Area Under the time-dependent ROC Curve (AUC) in the context of survival data, accommodating scenarios with right censored data and the option to account for competing risks. In addition to the ROC/AUC estimation, the package also estimates time-dependent Brier score and survival difference. Confidence intervals of various estimated quantities can be obtained from bootstrap. The package also offers plotting functions for visualizing time-dependent ROC curves.

Plots and analyzes time-intensity curve data, such as data from (contrast-enhanced) ultrasound. Values such as peak intensity, time to peak, area under the curve, wash in rate and wash out rate are calculated.

An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/07350015.2016.1200981>. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing high-order interactions among predictors. This package is based on the gbm package originally developed by Greg Ridgeway.

Estimates the parameters of a Transformed Ornstein-Uhlenbeck (TOU) stochastic model for adsorption data and also the parameters of the related pseudo-n-order (PNO) model, such as the maximum adsorption capacity (qe), the adsorption rate constant (kn) and the order of the model (n).

With the objective of including data from RSS feeds into your analysis, tidyRSS parses RSS, Atom and JSON feeds and returns a tidy data frame.

Evaluation of alternatives based on multiple criteria using TOPSIS method.