The library allows to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. Note that all these steps can be done separately which allows to implement new steps. Velasco, I., Sipols, A., de Blas, C. S., Pastor, L., & Bayona, S. (2023) <doi:10.1186/S12938-023-01079-X>. Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) <doi:10.1016/j.csda.2013.09.006>.

Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.).

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.

Implementation of Testlet and Item Response Theory. A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional and multidimensional Item Response Theory (IRT; Baker & Kim (2004) <doi:10.1201/9781482276725>) and Testlet Response Theory (TRT; Wainer et al., (2007) <doi:10.1017/CBO9780511618765>). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration. Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint Maximum Likelihood, the package also offers Bayesian estimation using priors with maximum a posteriori (MAP) method for unidimensional item response theory models. It also provides functions for scale linking and equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability across mixed-format test forms. It also facilitates fixed-parameter calibration, enabling users to estimate person abilities with known item parameters or vice versa, which is essential for pre-equating studies and item bank maintenance. Comprehensive data simulation functions are included to generate synthetic datasets with complex structures, including mixed-model blocks and specific testlet effects, aiding in methodological research and study design validation. Researchers can try multiple simulation situations.

Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics. On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>.

Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.

Several datasets which describe the chef contestants in Top Chef, the challenges that they compete in, and the results of those challenges. This data is useful for practicing data wrangling, graphing, and analyzing how each season of Top Chef played out.

Pacote para a analise de experimentos com um ou dois fatores com testemunhas adicionais conduzidos no delineamento inteiramente casualizado ou em blocos casualizados. "Package for the analysis of one or two-way experiments with additional controls conducted in a completely randomized design or in a randomized block design".

Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see <https://www.r-text.org>.

This package provides a collection of recipe datasets scraped from <https://www.allrecipes.com/>, containing two complementary datasets: allrecipes with 14,426 general recipes, and cuisines with 2,218 recipes categorized by country of origin. Both datasets include comprehensive recipe information such as ingredients, nutritional facts (calories, fat, carbs, protein), cooking times (preparation and cooking), ratings, and review metadata. All data has been cleaned and standardized, ready for analysis.

U.S. Department of the Treasury, Alcohol and Tobacco Tax and Trade Bureau (TTB) collects data and reports on monthly beer industry production and operations. This data package includes a collection of 10 years (2006 - 2015) worth of data on materials used at U.S. breweries in pounds reported by the Brewer's Report of Operations and the Quarterly Brewer's Report of Operations forms, ready for data analysis. This package also includes historical tax rates on distilled spirits, wine, beer, champagne, and tobacco products as individual data sets.

Providing new german-wide TapeR Models and functions for their evaluation. Included are the most common tree species in Germany (Norway spruce, Scots pine, European larch, Douglas fir, Silver fir as well as European beech, Common/Sessile oak and Red oak). Many other species are mapped to them so that 36 tree species / groups can be processed. Single trees are defined by species code, one or multiple diameters in arbitrary measuring height and tree height. The functions then provide information on diameters along the stem, bark thickness, height of diameters, volume of the total or parts of the trunk and total and component above-ground biomass. It is also possible to calculate assortments from the taper curves. Uncertainty information is provided for diameter, volume and component biomass estimation.

We present a range of simulations to aid researchers in determining appropriate sample sizes when performing critical thermal limits studies (e.g. CTmin/CTmin experiments). A number of wrapper functions are provided for plotting and summarising outputs from these simulations. This package is presented in van Steenderen, C.J.M., Sutton, G.F., Owen, C.A., Martin, G.D., and Coetzee, J.A. Sample size assessments for thermal physiology studies: An R package and R Shiny application. 2023. Physiological Entomology. <doi:10.1111/phen.12416>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/ThermalSampleR_Shiny/> , or it is accessible via GitHub at <https://github.com/clarkevansteenderen/ThermalSampleR_Shiny/>. We would like to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function.

The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. The model can also be run in a semi-distributed fashion and with dual representation of soil layer. The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. See Parajka, J., R. Merz, G. Bloeschl (2007) <DOI:10.1002/hyp.6253> Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.

Facilitate the movement between data frames to xts'. Particularly useful when moving from tidyverse to the widely used xts package, which is the input format of choice to various other packages. It also allows the user to use a spread_by argument for a character column xts conversion.

Pure R implementation of Apache Thrift. This library doesn't require any code generation. To learn more about Thrift go to <https://thrift.apache.org>.

Fits time-dependent shared frailty Cox model (specifically the adapted Paik et al.'s Model) based on the paper "Centre-Effect on Survival After Bone Marrow Transplantation: Application of Time-Dependent Frailty Models", by C.M. Wintrebert, H. Putter, A.H. Zwinderman and J.C. van Houwelingen (2004) <doi:10.1002/bimj.200310051>.

This package performs fast variable selection in high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level. The package is based on the paper Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>.

Utilizing the OpenAI API as the back end (<https://platform.openai.com/docs/api-reference>), TheOpenAIR offers R wrapper functions for the ChatGPT endpoint and several high-level functions that enable the integration of ChatGPT capabilities in diverse data-related tasks, such as data cleansing and automated analytics script generation.

Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences. These tools apply to traditional time-series quantities of interest: especially instantaneous effects for any period and cumulative effects for any period (including the long-run effect). They also allow for a variety of shock histories to be applied to the independent variable (beyond just a one-time, one-unit increase) as well as the recovery of inferences in levels for shocks applies to (in)dependent variables in differences (what we call the Generalized Dynamic Response Function). These effects are also available for the general conditional dynamic model advocated by Warner, Vande Kamp, and Jordan (2026 <doi:10.1017/psrm.2026.10087>). We also provide the actual formulae for these effects.

This package provides a set of fast tidy functions for wrangling, completing and summarising date and date-time data. It combines tidyverse syntax with the efficiency of data.table and speed of collapse'.

Tensor-train is a compact representation for higher-order tensors. Some algorithms for performing tensor-train decomposition are available such as TT-SVD, TT-WOPT, and TT-Cross. For the details of the algorithms, see I. V. Oseledets (2011) <doi:10.1137/090752286>, Yuan Longao, et al (2017) <doi:10.48550/arXiv.1709.02641>, I. V. Oseledets (2010) <doi:10.1016/j.laa.2009.07.024>.

Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data. Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.

The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.