Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.

This package provides a comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) <doi:10.21105/joss.00860>. For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) <doi:10.1140/epjds/s13688-017-0109-5>. To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>. To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at <https://github.com/Ripser/ripser>. This package has been published as Wadhwa et al. (2018) <doi:10.21105/joss.00860>.

Interface to TensorFlow Estimators <https://www.tensorflow.org/guide/estimator>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see for example Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>. This package provides an interface for fitting MMRM within the tern <https://cran.r-project.org/package=tern> framework by Zhu et al. (2023) and tabulate results easily using rtables <https://cran.r-project.org/package=rtables> by Becker et al. (2023). It builds on mmrm <https://cran.r-project.org/package=mmrm> by Sabanés Bové et al. (2023) for the actual MMRM computations.

Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by ggplot2 instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) <https://otexts.com/fpp3/>.

Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.

Uplift modeling aims at predicting the causal effect of an action such as a marketing campaign on a particular individual. In order to simplify the task for practitioners in uplift modeling, we propose a combination of tools that can be separated into the following ingredients: i) quantization, ii) visualization, iii) variable selection, iv) parameters estimation and, v) model validation. For more details, see <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.

This package implements combined p-value functions for two trials along with compatible combined point and interval estimates as described in Pawel, Roos, and Held (2025) <doi:10.48550/arXiv.2503.10246>.

Compute the coordinates to produce a tendril plot. In the tendril plot, each tendril (branch) represents a type of events, and the direction of the tendril is dictated by on which treatment arm the event is occurring. If an event is occurring on the first of the two specified treatment arms, the tendril bends in a clockwise direction. If an event is occurring on the second of the treatment arms, the tendril bends in an anti-clockwise direction. Ref: Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary of the incidence, significance and temporal aspects of adverse events in clinical trials" - JAMIA 2018; 25(8): 1069-1073 <doi:10.1093/jamia/ocy016>.

Calculates several thermal comfort indexes using temperature, wind speed and relative humidity values, calculating indexes such as Humidex, windchill, Discomfort Index and others.

This package provides an integrated user interface and workflow for the analysis of running, cycling and swimming data from GPS-enabled tracking devices through the trackeR <https://CRAN.R-project.org/package=trackeR> R package.

Fit Thurstonian Item Response Theory (IRT) models in R. This package supports fitting Thurstonian IRT models and its extensions using Stan', lavaan', or Mplus for the model estimation. Functionality for extracting results, making predictions, and simulating data is provided as well. References: Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>; Bürkner et al. (2019) <doi:10.1177/0013164419832063>.

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.

Create a time-varying dataset using features, exposure, and look back specifications.

Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.

Measuring tree architecture from terrestrial lidar data, including tree-level properties, crown characteristics, and structural attributes derived from quantitative structure models (QSMs).

Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. References used for this package: Foley, James, Andries van Dam, Steven Feiner, and John Hughes. "Computer Graphics: Principle and Practice". Addison-Wesley Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1); Genz, A., "Numerical computation of multivariate normal probabilities," Journal of Computational and Graphical Statistics, 1, 141-149 (1992) <doi:10.1080/10618600.1992.10477010>; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student- t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) <doi:10.1007/s11222-020-09978-y>; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilities with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 1-25 (2022) <doi:10.18637/jss.v101.i04>.

Targets parameters that solve Ordinary Differential Equations (ODEs) driven by a vector of cumulative hazard functions. The package provides a method for estimating these parameters using an estimator defined by a corresponding Stochastic Differential Equation (SDE) system driven by cumulative hazard estimates. By providing cumulative hazard estimates as input, the package gives estimates of the parameter as output, along with pointwise (co)variances derived from an asymptotic expression. Examples of parameters that can be targeted in this way include the survival function, the restricted mean survival function, cumulative incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018) <doi:10.1093/biomet/asy035>, and further applications in Stensrud, Røysland, and Ryalen (2019) <doi:10.1111/biom.13102> and Ryalen et al. (2021) <doi:10.1093/biostatistics/kxab009>.

Calculates empirical TL-moments (trimmed L-moments) of arbitrary order and trimming, and converts them to distribution parameters.

This package provides functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.

Testing, Implementation, and Forecasting of the THETA-SVM hybrid model. The THETA-SVM hybrid model combines the distinct strengths of the THETA model and the Support Vector Machine (SVM) model for time series forecasting.For method details see Bhattacharyya et al. (2022) <doi:10.1007/s11071-021-07099-3>.

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.

This package provides a traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.

Longitudinal data offers insights into population changes over time but often requires a flexible structure, especially with varying follow-up intervals. Panel data is one way to store such records, though it adds complexity to analysis. The tvtools package for R simplifies exploring and analyzing panel data.