This package provides a method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network, which is originally developed by Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided. The sample size of the dataset used to train the LSTM model is 1,000,000. Each sample is a batch of simulated response data with a specific latent factor structure. The eigenvalues of these response data will be used as sequential data to train the LSTM. The pre-trained LSTM is capable of factor retention for real response data with a true latent factor number ranging from 1 to 10, that is, determining the number of factors.

Estimates group transmission assortativity coefficients from transmission trees. Group transmission assortativity coefficients measure the tendency for individuals to transmit within their own group (e.g. age group, vaccination status, or location) compared to other groups. The package requires information on who infected whom, group membership for all individuals, and the relative sizes of each group in the population. For more details see Geismar et al. (2024) <doi:10.1371/journal.pone.0313037>.

The package converts R data onto input and data for LocalSolver, executes optimization and exposes optimization results as R data. LocalSolver (http://www.localsolver.com/) is an optimization engine developed by Innovation24 (http://www.innovation24.fr/). It is designed to solve large-scale mixed-variable non-convex optimization problems. The localsolver package is developed and maintained by WLOG Solutions (http://www.wlogsolutions.com/en/) in collaboration with Decision Support and Analysis Division at Warsaw School of Economics (http://www.sgh.waw.pl/en/).

An implementation of list comprehensions as purely syntactic sugar with a minor runtime overhead. It constructs nested for-loops and executes the byte-compiled loops to collect the results.

Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) <doi:10.1093/biostatistics/kxz052>.

Parse various reflectance/transmittance/absorbance spectra file formats to extract spectral data and metadata, as described in Gruson, White & Maia (2019) <doi:10.21105/joss.01857>. Among other formats, it can import files from Avantes <https://www.avantes.com/>, CRAIC <https://www.microspectra.com/>, and OceanOptics'/'OceanInsight <https://www.oceanoptics.com/> brands.

Generate concentration-time profiles from linear pharmacokinetic (PK) systems, possibly with first-order absorption or zero-order infusion, possibly with one or more peripheral compartments, and possibly under steady-state conditions. Single or multiple doses may be specified. Secondary (derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are computed.

Measure similarity between texts. Offers a variety of processing tools and similarity metrics to facilitate flexible representation of texts and matching. Implements forms of Language Style Matching (Ireland & Pennebaker, 2010) <doi:10.1037/a0020386> and Latent Semantic Analysis (Landauer & Dumais, 1997) <doi:10.1037/0033-295X.104.2.211>.

This package provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.

This package provides fast and scalable Gibbs sampling algorithms for Bayesian Lasso regression model in high-dimensional settings. The package implements efficient partially collapsed and nested Gibbs samplers for Bayesian Lasso, with a focus on computational efficiency when the number of predictors is large relative to the sample size. Methods are described at Davoudabadi and Ormerod (2026) <https://github.com/MJDavoudabadi/LassoHiDFastGibbs>.

Some simple objects and functions to do statistics using linear models and a Bayesian framework.

Generate and correlate synthetic Likert and rating-scale data with predefined means, standard deviations, Cronbach's Alpha, Factor Loading table, coefficients, and other summary statistics. Worked examples and documentation are available in the package articles, accessible via the package website, <https://winzarh.github.io/LikertMakeR/>.

Input latitude and longitude values or an sf/sfc POINT object and get back the time zone in which they exist. Two methods are implemented. One is very fast and uses Rcpp in conjunction with data from the Javascript library (<https://github.com/darkskyapp/tz-lookup-oss/>). This method also works outside of countries borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the sf package to intersect points with a detailed map of time zones from here: <https://github.com/evansiroky/timezone-boundary-builder/>. The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect.

This package provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.

Affords an alternative, vector-based syntax to lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow.

Extracts zip files, converts Word', Excel', and html'/'htm files to pdf format. Word and Excel conversion uses VBScript', while html'/'htm conversion uses webshot and PhantomJS'. Additionally, the package merges pdf files into a single document. This package is only supported on Windows due to VBScript dependencies.

This package provides tools for fast and accurate evaluation of skew stable distributions (CDF, PDF and quantile functions), random number generation, and parameter estimation. This is libstableR as per Royuela del Val, Simmross-Wattenberg, and Alberola López (2017) <doi:10.18637/jss.v078.i01> under a new maintainer.

An updated implementation of R package ranger by Wright et al, (2017) <doi:10.18637/jss.v077.i01> for training and predicting from random forests, particularly suited to high-dimensional data, and for embedding in Multiple Imputation by Chained Equations (MICE) by van Buuren (2007) <doi:10.1177/0962280206074463>. Ensembles of classification and regression trees are currently supported. Sparse data of class dgCMatrix (R package Matrix') can be directly analyzed. Conventional bagged predictions are available alongside an efficient prediction for MICE via the algorithm proposed by Doove et al (2014) <doi:10.1016/j.csda.2013.10.025>. Trained forests can be written to and read from storage. Survival and probability forests are not supported in the update, nor is data of class gwaa.data (R package GenABEL'); use the original ranger package for these analyses.

This package provides methods for assessing agreement between repeated measurements obtained by two or more methods using the longitudinal concordance correlation coefficient (LCC). Polynomial mixed-effects models (via nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) <doi:10.1007/s13253-018-0321-1>.

R6 classes to model traditional life insurance contracts like annuities, whole life insurances or endowments. Such life insurance contracts provide a guaranteed interest and are not directly linked to the performance of a particular investment vehicle, but they typically provide (discretionary) profit participation. This package provides a framework to model such contracts in a very generic (cash-flow-based) way and includes modelling profit participation schemes, dynamic increases or more general contract layers, as well as contract changes (like sum increases or premium waivers). All relevant quantities like premium decomposition, reserves and benefits over the whole contract period are calculated and potentially exported to Excel'. Mortality rates are given using the MortalityTables package.

Implementation of LT-FH++, an extension of the liability threshold family history (LT-FH) model. LT-FH++ uses a Gibbs sampler for sampling from the truncated multivariate normal distribution and allows for flexible family structures. LT-FH++ was first described in Pedersen, Emil M., et al. (2022) <doi:10.1016/j.ajhg.2022.01.009> as an extension to LT-FH with more flexible family structures, and again as the age-dependent liability threshold (ADuLT) model Pedersen, Emil M., et al. (2023) <https://www.nature.com/articles/s41467-023-41210-z> as an alternative to traditional time-to-event genome-wide association studies, where family history was not considered.

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

Lazy read for drawings. A dplyr back end for data sources supported by GDAL vector drivers, that allows working with local or remote sources as if they are in-memory data frames. Basic features works with any drawing format ('GDAL vector data source') supported by the sf package.

An object that supports automatic differentiation of matrix- and multidimensional-valued functions with respect to multidimensional independent variables. Automatic differentiation is via forward accumulation'.