Identifies clusters of individual longitudinal trajectories. In the spirit of Leffondre et al. (2004), the procedure involves identifying each trajectory to a point in the space of measures. In this context, a measure is a quantity meant to capture a certain characteristic feature of the trajectory. The points in the space of measures are then clustered using a version of spectral clustering.

This package provides a screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects. This package also provides two screening functions for screening high-dimensional predictors when the events are rare. The firth method is called Rare-Screening which employs a repeated random sampling with replacement and using linear modeling with Bayes adjustment. The Second method is called Firth-ttScreening which uses ttScreening method with additional Firth correction term in the maximum likelihood for the logistic regression model. These methods handle the high-dimensionality and low event rates.

This package provides a slightly-opinionated R interface for the Tremendous API (<https://www.tremendous.com/>). In addition to supporting GET and POST requests, tremendousr has, dare I say, tremendously intuitive functions for sending digital rewards and incentives directly from R.

Estimates the time-varying (tv) parameters of the GARCH(1,1) model, enabling the modeling of non-stationary volatilities by allowing the model parameters to change gradually over time. The estimation and prediction processes are facilitated through the application of the Kalman filter and state-space equations. This package supports the estimation of tv parameters for various deterministic functions, which can be identified through exploratory analysis of different time periods or segments of return data. The methodology is grounded in the framework presented by Ferreira et al. (2017) <doi:10.1080/00949655.2017.1334778>.

This package creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. treeheatr utilizes the customizable ggparty package for drawing decision trees.

Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>).

Trust region algorithm for nonlinear optimization. Efficient when the Hessian of the objective function is sparse (i.e., relatively few nonzero cross-partial derivatives). See Braun, M. (2014) <doi:10.18637/jss.v060.i04>.

This package creates a local Lightning Memory-Mapped Database ('LMDB') of many commonly used taxonomic authorities and provides functions that can quickly query this data. Supported taxonomic authorities include the Integrated Taxonomic Information System ('ITIS'), National Center for Biotechnology Information ('NCBI'), Global Biodiversity Information Facility ('GBIF'), Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). Name and identifier resolution using LMDB can be hundreds of times faster than either relational databases or internet-based queries. Precise data provenance information for data derived from naming providers is also included.

Goodness of Fit and Forecast Evaluation Tests for timeseries models. Includes, among others, the Generalized Method of Moments (GMM) Orthogonality Test of Hansen (1982), the Nyblom (1989) parameter constancy test, the sign-bias test of Engle and Ng (1993), and a range of tests for value at risk and expected shortfall evaluation.

This package contains performance analysis metrics of track records including entropy-based correlation and dynamic beta based on a state/space algorithm. The normalized sample entropy method has been implemented which produces accurate entropy estimation even on smaller datasets. On a separate stream, trades from the five major assets classes and also functionality to use pricing curves, rating tables, Credit Support Annex and add-on tables. The implementation follows an object oriented logic whereby each trade inherits from more abstract classes while also the curves/tables are objects. Furthermore, odds calculators and P&L back-testing functionality has been implemented for the most widely used betting/trading strategies including martingale, DAlembert', Labouchere and Fibonacci. Back testing has also been included for the EuroMillions', the EuroJackpot', the UK Lotto, the Set For Life and the UK ThunderBall lotteries. Furthermore, some basic functionality about climate risk has been included.

An R wrapper for the Spotify Web API <https://developer.spotify.com/web-api/>.

This package provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2024, <doi:10.1007/s41237-024-00228-3>). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) as well as functions for simulating various types of multistage designs.

This package provides threshold sweep methods for Qualitative Comparative Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an exploratory tool for discovering causal structures. Built on top of the QCA package by Dusa (2019) <doi:10.1007/978-3-319-75668-4>, with function arguments following QCA conventions. Based on set-theoretic methods by Ragin (2008) <doi:10.7208/chicago/9780226702797.001.0001> and established robustness protocols by Rubinson et al. (2019) <doi:10.1177/00491241211036158>.

When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting. The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) <doi:10.1080/00031305.2022.2063944>.

This is a companion package for the text2sdg package. It contains the trained ensemble models needed by the detect_sdg function from the text2sdg package. See Wulff, Meier and Mata (2023) <arXiv:2301.11353> and Meier, Wulff and Mata (2021) <arXiv:2110.05856> for reference.

This package provides a tufte'-alike style for rmarkdown'. A modern take on the Tufte design for pdf and html vignettes, building on the tufte package with additional contributions from the knitr and ggtufte package, and also acknowledging the key influence of envisioned css'.

Time series toolkit with identical behavior for all time series classes: ts','xts', data.frame', data.table', tibble', zoo', timeSeries', tsibble', tis or irts'. Also converts reliably between these classes.

This package provides classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.

Allows forecasting time series using nearest neighbors regression Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J. Rivera (2019) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. The nearest neighbors used in a prediction can be consulted and plotted.

This package provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using supervised machine learning techniques as lasso and elastic net.

Tracks parameter value, gradient, and Hessian at each iteration of numerical optimizers. Useful for analyzing optimization progress, diagnosing issues, and studying convergence behavior.

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

Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and tidyhte will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects.

Trelliscope is a scalable, flexible, interactive approach to visualizing data (Hafen, 2013 <doi:10.1109/LDAV.2013.6675164>). This package provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS. High-level functions are provided for creating displays from within tidyverse or ggplot2 workflows. Low-level functions are also provided for creating new interfaces.