Fits structural equation modeling via penalized likelihood.

Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.

This package provides a shiny application to automate forward and back survey translation with optional reconciliation using large language models (LLMs). Supports both item-by-item and batch translation modes for optimal performance and context-aware translations. Handles multi-sheet Excel files and supports OpenAI (GPT), Google Gemini, and Anthropic Claude models. Follows the TRAPD (Translation, Review, Adjudication, Pretesting, Documentation) framework and ISPOR (International Society for Pharmacoeconomics and Outcomes Research) recommendations. See Harkness et al. (2010) <doi:10.1002/9780470609927.ch7> and Wild et al. (2005) <doi:10.1111/j.1524-4733.2005.04054.x>.

This package provides a collection of colour palettes inspired by some of our dearest butterfly species. This package provides continuous and categorical palettes, including some colour blind friendly options.

Allows to install the R languageserver with all dependencies into a separate library and use that independent installation automatically when R is instantiated as a language server process. Useful for making language server seamless to use without running into package version conflicts.

This package provides a set of functions to locate some programs available on the user machine. The package provides functions to locate Node.js', npm', LibreOffice', Microsoft Word', Microsoft PowerPoint', Microsoft Excel', Python', pip', Mozilla Firefox and Google Chrome'. User can test the availability of a program with eventually a version and call it with function system2() or system(). This allows the use of a single function to retrieve the path to a program regardless of the operating system and its configuration.

Simplify the loading matrix in factor models using the l1 criterion as proposed in Freyaldenhoven (2025) <doi:10.21799/frbp.wp.2020.25>. Given a data matrix, find the rotation of the loading matrix with the smallest l1-norm and/or test for the presence of local factors with main function local_factors().

This package contains a set of functions to create data libraries, generate data dictionaries, and simulate a data step. The libname() function will load a directory of data into a library in one line of code. The dictionary() function will generate data dictionaries for individual data frames or an entire library. And the datestep() function will perform row-by-row data processing.

Framework for adding authentication to shiny applications. Provides flexibility as compared to other options for where user credentials are saved, allows users to create their own accounts, and password reset functionality. Bryer (2024) <doi:10.5281/zenodo.10987876>.

For fitting Bayesian joint latent class and regression models using Gibbs sampling. See the documentation for the model. The technical details of the model implemented here are described in Elliott, Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham, Belinda L., "Methods to account for uncertainty in latent class assignments when using latent classes as predictors in regression models, with application to acculturation strategy measures" (2020) In press at Epidemiology <doi:10.1097/EDE.0000000000001139>.

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>.

Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.

It is an extension of lmom R package: pel...()','cdf...()',qua...() function families are lumped and called from one function per each family respectively in order to create robust automatic tools to fit data with different probability distributions and then to estimate probability values and return periods. The implemented functions are able to manage time series with constant and/or missing values without stopping the execution with error messages. The package also contains tools to calculate several indices based on variability (e.g. SPI , Standardized Precipitation Index, see <https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi> and <http://spei.csic.es/>) for multiple time series or spatially gridded values.

This package provides the OpenEXR static library and C++ headers for high-dynamic-range image I/O (see <https://openexr.com/>) needed to link R packages against the OpenEXR library, along with a basic R interface to load EXR images.

This package contains 128 palettes from Color Lisa. All palettes are based on masterpieces from the worlds greatest artists. For more information, see <http://colorlisa.com/>.

This package provides utilities to detect common data leakage patterns including train/test contamination, temporal leakage, and data duplication, enhancing model reliability and reproducibility in machine learning workflows. Generates diagnostic reports and visual summaries to support data validation. Methods based on best practices from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0387848570).

This package contains some functions to learn and teach basic plane Geometry at undergraduate level with the aim of being helpful to young students with little programming skills.

Each function replaces multiple standard R functions. For example, two function calls, Read() and CountAll(), generate summary statistics for all variables in the data frame, plus histograms and bar charts. Other functions provide data aggregation via pivot tables; comprehensive regression, ANOVA, and t-test; visualizations including integrated Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve; reading multiple data formats with the same call; variable labels; time series with aggregation and forecasting; color themes; and Trellis (facet) graphics. Also includes a confirmatory factor analysis of multiple-indicator measurement models, pedagogical routines for data simulation (e.g., Central Limit Theorem), generation and rendering of regression instructions for interpretative output, and both interactive construction of visualizations and interactive visualizations with plotly.

For high-dimensional correlated observations, this package carries out the L_1 penalized maximum likelihood estimation of the precision matrix (network) and the correlation parameters. The correlated data can be longitudinal data (may be irregularly spaced) with dampening correlation or clustered data with uniform correlation. For the details of the algorithms, please see the paper Jie Zhou et al. Identifying Microbial Interaction Networks Based on Irregularly Spaced Longitudinal 16S rRNA sequence data <doi:10.1101/2021.11.26.470159>.

This package provides S3 classes to represent low rank matrix decompositions.

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.

Calculate point estimates of and valid confidence intervals for longitudinal summaries of nonparametric, algorithm-agnostic variable importance measures. For more details, see Williamson et al. (2024) <doi:10.48550/arXiv.2311.01638>.

Insieme di funzioni di supporto al volume "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. This package contains sets of functions defined in "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. Function names and docs are in italian as well.

This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.