This package provides a general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. tsfgrnn allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. 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. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation.

Compute age-adjusted rates by direct and indirect methods and other epidemiological indicators in a tidy way, wrapping functions from the epitools package.

Collection of ancillary functions and utilities to be used in conjunction with the TraMineR package for sequence data exploration. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as methods for polyadic data and for the comparison of groups of sequences.

This package provides functions to get personal Google Scholar profile data from web API and show it in table or figure format.

This package implements the truncated harmonic mean estimator (THAMES) of the reciprocal marginal likelihood using posterior samples and unnormalized log posterior values via reciprocal importance sampling. Metodiev, Perrot-Dockès, Ouadah, Irons, Latouche, & Raftery (2024). Bayesian Analysis. <doi:10.1214/24-BA1422>.

This contains functions that can be used to estimate the time-dependent precision-recall curve (PRC) and the corresponding area under the PRC for right-censored survival data. It also compute time-dependent ROC curve and its corresponding area under the ROC curve (AUC). See Beyene, Chen and Kifle (2024) <doi:10.1002/bimj.202300135>.

The companion package that provides all the datasets used in the book "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).

Operators and functions provided by base R sometimes lack some features found in other programming languages, such as the ability to concatenate strings using + or to repeat strings using *. This package aims at providing such functionality without breaking existing code, i.e., only statements, that would throw errors in pure base R are patched.

Represent, visualize, describe and wrangle functional data in tidy data frames, building on the tf package. Provides data types for functional observations that work as columns in data frames, enabling manipulation with dplyr verbs and visualization with ggplot2 geoms designed for functional data.

Estimation of the SF-ACE, a Causal Inference estimand proposed in the paper "The Subtype-Free Average Causal Effect For Heterogeneous Disease Etiology" (soon on arXiv).

Interface to TensorFlow Probability', a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', GPU'). TensorFlow Probability includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.

The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.

This package provides tools to help developers and producers manipulate R objects and outputs. It includes tools for displaying results and objects, and for formatting them in the correct format.

Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed. See Blanche et al. (2013) <doi:10.1002/sim.5958> and references therein for the details of the methods implemented in the package.

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.

This package provides a simple interface to search available data provided by Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded based on a search result or from a cart file downloaded from Theia website.

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>.

This package provides a tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.

This package provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.

This package provides a Text mining toolkit for Chinese, which includes facilities for Chinese string processing, Chinese NLP supporting, encoding detecting and converting. Moreover, it provides some functions to support tm package in Chinese.

This package provides a user-friendly R data package that is intended to make Turkish higher education statistics more accessible.

Plot official statistics time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. tstools comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, tstools provides a super fast, data.table backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.

This package provides tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries GUDHI <https://project.inria.fr/gudhi/software/>, Dionysus <https://www.mrzv.org/software/dionysus/>, and PHAT <https://bitbucket.org/phat-code/phat/>. This package also implements methods from Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2015) <doi:10.20382/jocg.v6i2a8> for analyzing the statistical significance of persistent homology features.

This package implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).