This package provides a unified interface to large language models across multiple providers. Supports text generation, structured output with optional JSON Schema validation, and embeddings. Includes tidyverse-friendly helpers, chat session, consistent error handling, and parallel batch tools.

This package provides functions to calculate lunar and other related environmental covariates.

This package provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.

This package provides a word embeddings-based semi-supervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>. LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove). It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.

Probabilistic record linkage without direct identifiers using only diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ; Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane & Cai (2021) <doi: 10.1093/jamia/ocab187>.

This package provides a bridge between the loon and ggplot2 packages. Extends the grammar of ggplot to add clauses to create interactive loon plots. Existing ggplot(s) can be turned into interactive loon plots and loon plots into static ggplot(s); the function loon.ggplot() is the bridge from one plot structure to the other.

This package provides a collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. It accompanies Navarro, D. J. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.6.

Data used as examples in the loon package.

This package provides functions to prepare, visualize, and analyse diachronic network data on local political actors, with a particular focus on the development of local party systems and identification of actor groups. Formalizes and automates a continuity diagram method that has been previously applied in research on Czech local politics, e.g. Bubenicek and Kubalek (2010, ISSN:1803-8220), Kubalek and Bubenicek (2012, ISSN:1803-8220), and Cmejrek, Bubenicek, and Copik (2010, ISBN:978-80-247-3061-5). The package also includes several example datasets derived from Czech municipal elections, compiled from official election results, field research, and previously published case studies on Czech local politics.

Estimates marginal likelihood from a posterior sample using the method described in Wang et al. (2023) <doi:10.1093/sysbio/syad007>, which does not require evaluation of any additional points and requires only the log of the unnormalized posterior density for each sampled parameter vector.

Prototypes for construction of a Gaussian Stochastic Process emulator (GASP) of a computer model. This is done within the objective Bayesian implementation of the GASP. The package allows for construction of a linked GASP of the composite computer model. Computational implementation follows the mathematical exposition given in publication: Ksenia N. Kyzyurova, James O. Berger, Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, (2018).<DOI:10.1137/17M1157702>.

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is logConDens that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

Additional appenders for the logging package lgr that support logging to Elasticsearch', Dynatrace', AWSCloudWatchLog', databases, syslog', email- and push notifications, and more.

Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures.

This package provides a statistical learning method that tries to find the best set of predictors and interactions between predictors for modeling binary or quantitative response data in a decision tree. Several search algorithms and ensembling techniques are implemented allowing for finetuning the method to the specific problem. Interactions with quantitative covariables can be properly taken into account by fitting local regression models. Moreover, a variable importance measure for assessing marginal and interaction effects is provided. Implements the procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

This package provides functions for vectorised conditional recoding of variables. case_when() enables you to vectorise multiple if and else statements (like CASE WHEN in SQL'). if_else() is a stricter and more predictable version of ifelse() in base that preserves attributes. These functions are forked from dplyr with all package dependencies removed and behave identically to the originals.

R lists, especially nested lists, can be very difficult to visualize or represent. Sometimes str() is not enough, so this suite of htmlwidgets is designed to help see, understand, and maybe even modify your R lists. The function reactjson() requires a package reactR that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.

Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) <doi:10.1101/2022.10.28.513998>.

To decompose symmetric matrices such as brain connectivity matrices so that one can extract sparse latent component matrices and also estimate mixing coefficients, a blind source separation (BSS) method named LOCUS was proposed in Wang and Guo (2023) <arXiv:2008.08915>. For brain connectivity matrices, the outputs correspond to sparse latent connectivity traits and individual-level trait loadings.

Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast.

Split your rmarkdown or quarto files by sections into a tibble: titles, text, chunks. Rebuild the file from the tibble.

This package contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.

Includes some procedures for latent variable modeling with a particular focus on multilevel data. The LAM package contains mean and covariance structure modelling for multivariate normally distributed data (mlnormal(); Longford, 1987; <doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm (amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 2014; <doi:10.1093/aje/kwt245>).

Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the LongCART and SurvCART algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.