The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

Modifying a load shape to match specific peak and load factor is a fundamental component for various power system planning and operation studies. This package is an efficient tool to modify a reference load shape while matching the desired peak and load factor. The package offers both linear and non-linear method, described in <https://rpubs.com/riazakhan94/load_shape_match_peak_energy>. The user can control the shape of the final load shape by regulating certain parameters. The package provides validation metrics for assessing the derived load shape in terms of preserving time series properties. It also offers powerful graphics, that allows the user to visually assess the derived load shape.

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>.

This package produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.

Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the lme4 machinery. The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen glue'.

Connect to the Less Annoying CRM API with ease to get your crm data in a clean and tidy format. Less Annoying CRM is a simple CRM built for small businesses, more information is available on their website <https://www.lessannoyingcrm.com/>.

This package contains functions to help create log files. The package aims to overcome the difficulty of the base R sink() command. The log_print() function will print to both the console and the file log, without interfering in other write operations.

This package provides classes and methods that allow the user to manage life table, actuarial tables (also multiple decrements tables). Moreover, functions to easily perform demographic, financial and actuarial mathematics on life contingencies insurances calculations are contained therein. See Spedicato (2013) <doi:10.18637/jss.v055.i10>.

Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.

Assess the proportion of treatment effect explained by a longitudinal surrogate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>; and estimate the treatment effect on a longitudinal surrogate marker as described in Wang et al. (2025) <doi:10.1093/biomtc/ujaf104>. A tutorial for this package can be found at <https://www.laylaparast.com/longsurr>.

The programs were developed for estimation of parameters and testing exponential versus Pareto distribution during our work on hydrologic extremes. See Kozubowski, T.J., A.K. Panorska, F. Qeadan, and A. Gershunov (2007) <doi:10.1080/03610910802439121>, and Panorska, A.K., A. Gershunov, and T.J. Kozubowski (2007) <doi:10.1007/978-0-387-34918-3_26>.

Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.

This package provides a toolbox for R arrays. Flexibly split, bind, reshape, modify, subset and name arrays.

This package contains different algorithms and construction methods for optimal Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.

This package provides extensions to the leaflet package to customize legends with images, text styling, orientation, sizing, and symbology and functions to create symbols to plot on maps.

Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices.

This package creates lowpass filters which are commonly used in ion channel recordings. It supports generation of random numbers that are filtered, i.e. follow a model for ion channel recordings, see <doi:10.1109/TNB.2018.2845126>. Furthermore, time continuous convolutions of piecewise constant signals with the kernel of lowpass filters can be computed.

Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han S., et al. (2019) <doi:10.1093/bib/bby065>.

Generates quotes from Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.

Trend filtering is a widely used nonparametric method for knot detection. This package provides an efficient solution for L0 trend filtering, avoiding the traditional methods of using Lagrange duality or Alternating Direction Method of Multipliers algorithms. It employ a splicing approach that minimizes L0-regularized sparse approximation by transforming the L0 trend filtering problem. The package excels in both efficiency and accuracy of trend estimation and changepoint detection in segmented functions. References: Wen et al. (2020) <doi:10.18637/jss.v094.i04>; Zhu et al. (2020)<doi:10.1073/pnas.2014241117>; Wen et al. (2023) <doi:10.1287/ijoc.2021.0313>.

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>.

Maximum likelihood estimation and likelihood ratio test are essential for modern statistics. This package supports in calculating likelihood based inference. Reference: Pawitan Y. (2001, ISBN:0-19-850765-8).

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).