Framework provides functions to parse Training Center XML (TCX) files and extract key activity metrics such as total distance, total time, calories burned, maximum altitude, and power values (watts). This package is useful for analyzing workout and training data from devices that export TCX format.

Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.

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>.

Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', Stan', rstanarm', brms', MCMCglmm', coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, ggplot2 geoms and stats are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands.

We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) <doi:10.1214/17-AOAS1027>, Tak et al. (2018) <doi:10.1080/10618600.2017.1415911>, Hu and Tak (2020) <arXiv:2005.08049>.

Wrapper for using tapkee command line utility, it allows to run it from inside R and catch the results for further analysis and plotting. Tapkee is a program for fast dimension reduction, see package?tapkee and <http://tapkee.lisitsyn.me/> for installation and other details.

The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data. The algorithm is described in details in Lavenus et al., Plant Cell, 2015. It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana. The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.

Runs targets pipeline in /inst/tarchives and stores the results in the R user directory. This means that the user does not have to run the process repeatedly, and the developer has the flexibility to update the data as versions are updated.

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

This package provides functions for the selection of thresholds for use in extreme value models, based mainly on the methodology in Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>. It also performs predictive inferences about future extreme values, based either on a single threshold or on a weighted average of inferences from multiple thresholds, using the revdbayes package <https://cran.r-project.org/package=revdbayes>. At the moment only the case where the data can be treated as independent identically distributed observations is considered.

Processing and analysis of pathomics, omics and other medical datasets. tRigon serves as a toolbox for descriptive and statistical analysis, correlations, plotting and many other methods for exploratory analysis of high-dimensional datasets.

Utilizing the logger framework to record events within a package, specific to teal family of packages. Supports logging namespaces, hierarchical logging, various log destinations, vectorization, and more.

Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time. These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) <doi:10.1037/a0025814>. We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

This package contains summary data on gene expression in normal human tissues from the Human Protein Atlas for use with the Tissue-Adjusted Pathway Analysis of cancer (TPAC) method. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.

This package provides color palettes corresponding to professional and amateur, sports teams. These can be useful in creating data graphics that are themed for particular teams.

This package implements tic-tac-toe game to play on console, either with human or AI players. Various levels of AI players are trained through the Q-learning algorithm.

Instance feature calculation and evolutionary instance generation for the traveling salesman problem. Also contains code to "morph" two TSP instances into each other. And the possibility to conveniently run a couple of solvers on TSP instances.

The twelvedata REST service offers access to current and historical data on stocks, standard as well as digital crypto currencies, and other financial assets covering a wide variety of course and time spans. See <https://twelvedata.com/> for details, to create an account, and to request an API key for free-but-capped access to the data.

Facilitates development and application of two-regression algorithms for research-grade wearable devices. It provides an easy way for users to access previously-developed algorithms, and also to develop their own. Initial motivation came from Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2018) <doi:10.1249/MSS.0000000000001532>. However, other algorithms are now supported. Please see the associated references in the package documentation for full details of the algorithms that are supported.

This package provides a set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. TensorPreAve provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. See Chen and Lam (2023) <arXiv:2208.04012> for more details.

This package provides functions for point and interval estimation in error-in-variables models via total least squares or generalized total least squares method. See Golub and Van Loan (1980) <doi:10.1137/0717073>, Gleser (1981) <https://www.jstor.org/stable/2240867>, Ivan Markovsky and Huffel (2007) <doi:10.1016/j.sigpro.2007.04.004> for more information.

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 analytical framework is based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).

This package provides several confidence interval and testing procedures, based on either semiparametric (using event-specific win ratios) or nonparametric measures, including the ratio of integrated cumulative hazard (RICH) and the ratio of integrated transformed cumulative hazard (RITCH), for treatment effect inference with terminal and non-terminal events under competing risks. The semiparametric results were developed in Yang et al. (2022 <doi:10.1002/sim.9266>), and the nonparametric results were developed in Yang (2025 <doi:10.1002/sim.70205>). For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 <doi:10.1002/(SICI)1097-0258(19990615)18:11%3C1341::AID-SIM129%3E3.0.CO;2-7>), Pocock et al. 2012 <doi:10.1093/eurheartj/ehr352>, and Bebu and Lachin 2016 <doi:10.1093/biostatistics/kxv032>) are included. The package also supports univariate survival analysis with a single event. In this package, effect size estimates and confidence intervals are obtained for each event type, and several testing procedures are implemented for the global null hypothesis of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazards assumptions, under which the event-specific win ratios converge to hazard ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment effect across all events, confidence intervals for linear combinations of the event-specific win ratios, RICH, or RITCH are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al. (2022 <doi:10.1002/sim.9266>) and Yang (2025 <doi:10.1002/sim.70205>).