This package provides a suite of functions to ping URLs and to time HTTP requests'. Designed to work with httr'.

This package provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2025) <doi:10.1080/07350015.2025.2526424>.

HAR files are generated and consumed by GEMPACK applications. This package reads/writes HAR files (and SL4 files) directly using basic R functions.

Enables chat completion and text annotation with local and OpenAI <https://openai.com/> language models, supporting batch processing, multiple annotators, and consistent output formats.

Solves Hidden Markov Models (HMMs) via matrix and tensor decomposition. Converts observation sequences to co-occurrence matrices/tensors and applies Symmetric Non-negative Matrix Factorization (symNMF), Singular Value Decomposition (SVD), CANDECOMP/PARAFAC (CP) decomposition, or Tensor-Train (TT) decomposition to recover HMM parameters. Also provides standard HMM algorithms (Forward, Backward, Viterbi, Baum-Welch) for comparison. The spectral learning approach for HMMs is based on Hsu, Kakade, and Zhang (2012) <doi:10.1016/j.jcss.2011.12.025>. The symNMF method is described in Kuang, Yun, and Park (2015) <doi:10.1007/s10898-014-0247-2>. The Tensor-Train decomposition is described in Oseledets (2011) <doi:10.1137/090752286>.

This package provides a lightweight framework for building server-driven web applications in R'. htmxr combines the simplicity of htmx for partial page updates with the power of plumber2 for non-blocking HTTP endpoints. Build interactive dashboards and data applications without writing JavaScript', using familiar R patterns inspired by Shiny'. For more information on htmx', see <https://htmx.org>.

Makes it easy to extract and combine variables from the HILDA (Household, Income and Labour Dynamics in Australia) survey maintained by the Melbourne Institute <https://melbourneinstitute.unimelb.edu.au/hilda>.

This package implements the simpler and faster heat index, which matches the values of the original 1979 heat index and its 2022 extension for air temperatures above 300 K (27 C, 80 F) and with only minor differences at lower temperatures. Also implements an algorithm for calculating the thermodynamic (and psychrometric) wet-bulb (and ice-bulb) temperature.

Perform hierarchical Bayesian Aldrich-McKelvey scaling using Hamiltonian Monte Carlo via Stan'. Aldrich-McKelvey ('AM') scaling is a method for estimating the latent positions of survey respondents and external objects on a common scale using positional survey data. The hierarchical versions of the Bayesian AM model included in this package outperform other versions both in terms of yielding meaningful posterior distributions for respondent positions and in terms of recovering true respondent positions in simulations. The package contains functions for preparing data, fitting models, extracting estimates, plotting key results, and comparing models using cross-validation. The original version of the default model is described in Bølstad (2024) <doi:10.1017/pan.2023.18>.

In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. The methods and the basic statistics for histogram-valued data are mainly based on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric between quantile functions. The package contains unsupervised classification techniques, least square regression and tools for histogram-valued data and for histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

This package provides functions implementing change point detection methods using the maximum pairwise Bayes factor approach. Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques.

Several handy plots for quickly looking at the relationship between two numeric vectors of equal length. Quickly visualize scatter plots, residual plots, qq-plots, box plots, confidence intervals, and prediction intervals.

Functions, data sets, analyses and examples from the book A Handbook of Statistical Analyses Using R (Brian S. Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2006). The first chapter of the book, which is entitled An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available.

Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016. Additional data sets containing geographical information and links to Wikipedia are also included.

Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.

Manipulate data through memory-mapped files, as vectors, matrices or arrays. Basic arithmetic functions are implemented, but currently no matrix arithmetic. Can write and read descriptor files for compatibility with the bigmemory package.

This package provides a tool to format R markdown with CSS ids for HTML output. The tool may be most helpful for those using markdown to create reproducible documents. The biggest limitations in formatting is the knowledge of CSS by the document authors.

This package implements the Brakerski-Fan-Vercauteren (BFV, 2012) <https://eprint.iacr.org/2012/144>, Brakerski-Gentry-Vaikuntanathan (BGV, 2014) <doi:10.1145/2633600>, and Cheon-Kim-Kim-Song (CKKS, 2016) <https://eprint.iacr.org/2016/421.pdf> schema for Fully Homomorphic Encryption. The included vignettes demonstrate the encryption procedures.

In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments. It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts. Methods for concept drift detection are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.

Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).

This package contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) <doi:10.1111/rssc.12194>; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) <doi:10.1029/2017JB015360>. The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package HiddenMarkov by David Harte. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions.

Hadamard matrix based statistical designs are of immense importance as the resultant designs carry various desirable characterizing properties. Constructing Partially Balanced Incomplete Block Designs (PBIBds) using Kronecker product of incidence matrices of Balanced Incomplete Block (BIB) and Partially Balanced Incomplete Block (PBIB) designs is much evident from literature. Here, we have constructed Incomplete Block Designs (IBDs) based on Hadamard matrices and Kronecker product of Hadamard matrices.

Nonparametric cumulative-incidence based estimation of the ratios of sub-hazard ratios to cause-specific hazard ratios using the approach from Ng et al. (2020).

Implementation of Bayesian regressions over the meta-d model of psychological data from two alternative forced choice tasks with ordinal confidence ratings. For more information, see Maniscalco & Lau (2012) <doi:10.1016/j.concog.2011.09.021>. The package is a front-end to the brms package, which facilitates a wide range of regression designs, as well as tools for efficiently extracting posterior estimates, plotting, and significance testing.