The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on keras and tensorflow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

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>.

Unsupervised text tokenizer focused on computational efficiency. Wraps the YouTokenToMe library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://aclanthology.org/P16-1162/>.

This package provides a pure interface for the Telegram Bot API <http://core.telegram.org/bots/api>. In addition to the pure API implementation, it features a number of tools to make the development of Telegram bots with R easy and straightforward, providing an easy-to-use interface that takes some work off the programmer.

Variant determination and genotyping from high throughput sequences from multilocus amplicon libraries, typically sequenced in Illumina MiSeq or similar. It provides a set of core functions for the central steps: demultiplex by locus, truncate reads, variant calling, and genotype calling. Additionally, it provides a set of functions for diagnosis and estimation of best running parameters and multiple extensions for genotype/variants manipulation and reformatting. Output variants and genotypes are output in tidy format, thus facilitating reformatting, manipulation and potential connection to other R packages.

This package performs model-based tensor clustering methods including Tensor Gaussian Mixture Model (TGMM), Tensor Envelope Mixture Model (TEMM) by Deng and Zhang (2021) <DOI: 10.1111/biom.13486>, Doubly-Enhanced EM (DEEM) algorithm by Mai, Zhang, Pan and Deng (2021) <DOI: 10.1080/01621459.2021.1904959>.

Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree heights are not measured, methods for estimating additional variance in volume predictions resulting from uncertainties in tree height models (tariffs) are provided. The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013) <doi:10.1007/s10342-013-0715-0>.

Implement text and sentiment analysis with texter'. Generate sentiment scores on text data and also visualize sentiments. texter allows you to quickly generate insights on your data. It includes support for lexicons such as NRC and Bing'.

This package provides a toolbox for comparing two data frames. This package is defunct. I recommend you use the "versus" package instead.

This package provides a lightweight and focused text annotation tool built with shiny'. Provides an interactive graphical user interface for coding text documents, managing code hierarchies, creating memos, and analyzing coding patterns. Features include code co-occurrence analysis, visualization of coding patterns, comparison of multiple coding sets, and export capabilities. Supports collaborative qualitative research through standardized annotation formats and analysis tools.

Analyze telemetry datasets generalized to allow any technology. The filtering steps check for false positives caused by reflected transmissions from surfaces and false pings from other noise generating equipment. The filters are based on JSATS filtering algorithms found in package filteRjsats <https://CRAN.R-project.org/package=filteRjsats> but have been generalized to allow the user to define many of the filtering variables. Additionally, this package contains scripts used to help identify an optimal maximum blanking period as defined in Capello et al (2015) <doi:10.1371/journal.pone.0134002>. The functions were written according to their manuscript description, but have not been reviewed by the authors for accuracy. It is included here as is, without warranty.

Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. TDApplied provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.

This package contains R functions for simulating and estimating integer-valued trawl processes as described in the article Veraart (2019),"Modeling, simulation and inference for multivariate time series of counts using trawl processes", Journal of Multivariate Analysis, 169, pages 110-129, <doi:10.1016/j.jmva.2018.08.012> and for simulating random vectors from the bivariate negative binomial and the bi- and trivariate logarithmic series distributions.

Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.

Introduction of qenv S4 class, that facilitates code execution and reproducibility in teal applications.

This package provides functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.

Interface to TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.

Extends invariant causal prediction (Peters et al., 2016, <doi:10.1111/rssb.12167>) to generalized linear and transformation models (Hothorn et al., 2018, <doi:10.1111/sjos.12291>). The methodology is described in Kook et al. (2023, <doi:10.1080/01621459.2024.2395588>).

R implementation of the software tools developed in the H-CUP (Healthcare Cost and Utilization Project) <https://hcup-us.ahrq.gov> and AHRQ (Agency for Healthcare Research and Quality) <https://www.ahrq.gov>. It currently contains functions for mapping ICD-9 codes to the AHRQ comorbidity measures and translating ICD-9 (resp. ICD-10) codes to ICD-10 (resp. ICD-9) codes based on GEM (General Equivalence Mappings) from CMS (Centers for Medicare and Medicaid Services).

Features include the ability to extract tabled content from NISO-JATS-coded XML, any native HTML or HML file, DOCX, and PDF documents, and then collapse it into a text format that is readable by humans by mimicking the actions of a screen reader. As tables within PDF documents are extracted with the tabulapdf package, and the table captions and footnotes cannot be extracted, the results on tables within PDF documents have to be considered less precise. The function table2matrix() returns a list of the tables within a document as character matrices. table2text() collapses the matrix content into a list of character strings by imitating the behavior of a screen reader. The textual representation of characters and numbers can be unified with unifyMatrix() before parsing. The function table2stats() extracts the tabled statistical test results from the collapsed text with the function standardStats() from the JATSdecoder package and, if activated, checks the reported and coded p-values for consistency. Due to the great variability and potential complexity of table structures, parsing accuracy may vary. A detailed description of how tableParser works is provided here: <doi:10.48550/arXiv.2603.19756>.

Calculates Zhong's optimal two-/three-stage Phase II designs for single-arm trials, generates target-toxicity decision tables for two-/three-stage Phase I dose-finding, and supports dose-finding simulations using custom decision tables. The Phase II design is based on Zhong (2012) <doi:10.1016/j.cct.2012.07.006>.

This package implements the TRUH test statistic for two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020). <DOI:10.1214/20-AOAS1362> for more details.

Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally mechanistic, with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models.

The best ANN structure for time series data analysis is a demanding need in the present era. This package will find the best-fitted ANN model based on forecasting accuracy. The optimum size of the hidden layers was also determined after determining the number of lags to be included. This package has been developed using the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.