Constructs likelihood models from heterogeneous observation types by composing named contributions. Each observation type (exact, left-censored, right-censored, interval-censored, or custom) contributes independently to the total log-likelihood, which is summed under an i.i.d. assumption. Provides contr_name() for standard R distributions and contr_fn() for user-defined contributions, composed via likelihood_contr() into objects compatible with the likelihood.model inference framework.

This package provides functions and tools for using open GIS and remote sensing command-line interfaces in a reproducible environment.

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.

Create lipidome-wide heatmaps of statistics with the lipidomeR'. The lipidomeR provides a streamlined pipeline for the systematic interpretation of the lipidome through publication-ready visualizations of regression models fitted on lipidomics data. With lipidomeR', associations between covariates and the lipidome can be interpreted systematically and intuitively through heatmaps, where lipids are categorized by the lipid class and are presented on two-dimensional maps organized by the lipid size and level of saturation. This way, the lipidomeR helps you gain an immediate understanding of the multivariate patterns in the lipidome already at first glance. You can create lipidome-wide heatmaps of statistical associations, changes, differences, variation, or other lipid-specific values. The heatmaps are provided with publication-ready quality and the results behind the visualizations are based on rigorous statistical models.

This is an extension package to logrx', which is a log creation program focused on Clinical Reporting within the Pharma Industry. This package enables a simple shiny-based Add-in that provides a point and click interface to produce a log for a single program.

This package provides a minimal-dependency client for Large Language Model chat APIs. Supports OpenAI <https://openai.com/>, Anthropic Claude <https://claude.com/>, Moonshot Kimi <https://www.moonshot.ai/>, OpenAI Codex subscription endpoints, Ollama <https://ollama.com/>, and other OpenAI'-compatible endpoints. Includes an agent loop with tool use and a Model Context Protocol client <https://modelcontextprotocol.io/>. API design is derived from the ellmer package, reimplemented with only base R, curl', jsonlite', and tinyoauth'.

Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use.

Interpretability of complex machine learning models is a growing concern. This package helps to understand key factors that drive the decision made by complicated predictive model (so called black box model). This is achieved through local approximations that are either based on additive regression like model or CART like model that allows for higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>. More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.

Profiles political leaders at a distance from text data such as speeches, interviews, press conferences, and other public statements. Computes Leadership Trait Analysis scores for seven personality traits -- including need for power, conceptual complexity, and self-confidence -- and classifies leaders into one of eight leadership styles. Also computes Operational Code Analysis scores summarising a leader's beliefs about politics and the use of power.

Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). LDA calculates the pairwise linkage disequilibrium of ancestry between single nucleotide polymorphisms (SNPs). LDAS calculates the LDA score of SNPs. The methods are described in Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>.

Inference for the Lorenz and penalized Lorenz regressions. More broadly, the package proposes functions to assess inequality and graphically represent it. The Lorenz Regression procedure is introduced in Heuchenne and Jacquemain (2022) <doi:10.1016/j.csda.2021.107347> and in Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024) <doi:10.1214/23-EJS2200>.

This package performs recursive partitioning of linear and nonlinear mixed effects models, specifically for longitudinal data. The package is an extension of the original longRPart package by Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.

Estimate the slope and intercept of a bivariate linear relationship by calculating a posterior density that is invariant to interchange and scaling of the coordinates.

This package implements local polynomial distribution and density methods for point estimation, inference and bandwidth selection, documented in Cattaneo, Jansson and Ma (2020) <doi:10.1080/01621459.2019.1635480>, Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>, and Cattaneo, Jansson and Ma (2024) <doi:10.1016/j.jeconom.2021.01.006>. lpdensity() constructs local polynomial distribution and density estimators with robust bias-corrected inference, and lpbwdensity() implements data-driven bandwidth selection.

Calculates cost surfaces based on slope to be used when modelling pathways and movement potential within a landscape (Lewis, 2021) <doi:10.1007/s10816-021-09522-w>.

Estimation of the local false discovery rate using the method of moments.

When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.

Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in Onofri et al (2020) <DOI:10.1007/s00122-020-03716-8>.

Fit linear models based on periodic splines, moderate model coefficients using multivariate adaptive shrinkage, then compute properties of the moderated curves.

This package implements Latent Unknown Clusters By Integrating Multi-omics Data (LUCID; Peng (2019) <doi:10.1093/bioinformatics/btz667>) for integrative clustering with exposures, multi-omics data, and health outcomes. Supports three integration strategies: early, parallel, and serial. Provides model fitting and tuning, lasso-type regularization for exposure and omics feature selection, handling of missing data, including both sporadic and complete-case patterns, prediction, and g-computation for estimating causal effects of exposures, bootstrap inference for uncertainty estimation, and S3 summary and plot methods. For the multi-omics integration framework, see Jia (2024) <https://journal.r-project.org/articles/RJ-2024-012/RJ-2024-012.pdf>. For the missing-data imputation mechanism, see Jia (2024) <doi:10.1093/bioadv/vbae123>.

Nonparametric methods for landmark prediction of long-term survival outcomes, incorporating covariate and short-term event information. The package supports the construction of flexible varying-coefficient models that use discrete covariates, as well as multiple continuous covariates. The goal is to improve prediction accuracy when censored short-term events are available as predictors, using robust nonparametric procedures that do not require correct model specification and avoid restrictive parametric assumptions found in alternative methods. More information on these methods can be found in Parast et al. 2012 <doi:10.1080/01621459.2012.721281>, Parast et al. 2011 <doi:10.1002/bimj.201000150>, and Parast and Cai 2013 <doi:10.1002/sim.5776>. A tutorial for this package is available here: <https://www.laylaparast.com/landpred>.

When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. lime (a port of the lime Python package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) <doi:10.48550/arXiv.1602.04938>.

An approach to analyzing Likert response items, with an emphasis on visualizations. The stacked bar plot is the preferred method for presenting Likert results. Tabular results are also implemented along with density plots to assist researchers in determining whether Likert responses can be used quantitatively instead of qualitatively. See the likert(), summary.likert(), and plot.likert() functions to get started.

Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize the model without losing any information, and fits the model on the standard fitting algorithm with no convergence issues.