This package provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) <arXiv:2006.01340>.

This package performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.

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>.

Crabs in the English channel, deer skulls, English monarchs, half-caste Manga characters, Jamaican cities, Shakespeare's The Tempest, drugged up cyclists and sexually transmitted diseases.

Estimate linear quantile mixtures based on Time-Constant (TC) and/or Time-Varying (TV), discrete, random coefficients.

Impute observed values below the limit of detection (LOD) via censored likelihood multiple imputation (CLMI) in single-pollutant models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>. CLMI handles exposure detection limits that may change throughout the course of exposure assessment. lodi provides functions for imputing and pooling for this method.

Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag, Wagner and Zeileis (2019) <doi:10.1111/geer.12146>.

This package provides a collection of helper functions for multiple regression models fitted by lm(). Most of them are simple functions for simple tasks which can be done with coding, but may not be easy for occasional users of R. Most of the tasks addressed are those sometimes needed when using the manymome package (Cheung and Cheung, 2023, <doi:10.3758/s13428-023-02224-z>) and stdmod package (Cheung, Cheung, Lau, Hui, and Vong, 2022, <doi:10.1037/hea0001188>). However, they can also be used in other scenarios.

This package provides tools for model specification in the latent variable framework (add-on to the lava package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.

This package implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively. The method is documented in Kumar et al (2013) <https://www.nature.com/articles/jes201352)>.

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.

This package provides tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. Broman et al. (2015) <doi:10.1534/g3.115.019778>.

The Lorentz transform in special relativity; also the gyrogroup structure of three-velocities. Performs active and passive transforms and has the ability to use units in which the speed of light is not unity. Includes some experimental functionality for celerity and rapidity. For general relativity, see the schwarzschild package.

Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.

Log-analytic methods intended for testing multiplicative effects.

Allows you to read and change the state of LIFX smart light bulbs via the LIFX developer api <https://api.developer.lifx.com/>. Covers most LIFX api endpoints, including changing light color and brightness, selecting lights by id, group or location as well as activating effects.

An educational package for teaching statistics and mathematics in both primary and higher education. The objective is to assist in the teaching/learning process, both for student study planning and teacher teaching strategies. The leem package aims to provide, in a simple yet in-depth manner, knowledge of statistics and mathematics to anyone who wants to study these areas of knowledge.

Lexical response data is a package that can be used for processing cued-recall, free-recall, and sentence responses from memory experiments.

This package provides functions that allow for convenient working with vector space models of semantics/distributional semantic models/word embeddings. Originally built for LSA models (hence the name), but can be used for all such vector-based models. For actually building a vector semantic space, use the package lsa or other specialized software. Downloadable semantic spaces can be found at <https://sites.google.com/site/fritzgntr/software-resources>.

Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. Details in McNicholas and Murphy (2010) <doi:10.1002/cjs.10047> and McNicholas and Subedi (2012) <doi:10.1016/j.jspi.2011.11.026>.

The goal of this package is to cover the most common steps in Loss Given Default (LGD) rating model development. The main procedures available are those that refer to bivariate and multivariate analysis. In particular two statistical methods for multivariate analysis are currently implemented â OLS regression and fractional logistic regression. Both methods are also available within different blockwise model designs and both have customized stepwise algorithms. Descriptions of these customized designs are available in Siddiqi (2016) <doi:10.1002/9781119282396.ch10> and Anderson, R.A. (2021) <doi:10.1093/oso/9780192844194.001.0001>. Although they are explained for PD model, the same designs are applicable for LGD model with different underlying regression methods (OLS and fractional logistic regression). To cover other important steps for LGD model development, it is recommended to use LGDtoolkit package along with PDtoolkit', and monobin (or monobinShiny') packages. Additionally, LGDtoolkit provides set of procedures handy for initial and periodical model validation.

Implementations of most of the existing proximity-based methods of link prediction in graphs. Among the 20 implemented methods are e.g.: Adamic L. and Adar E. (2003) <doi:10.1016/S0378-8733(03)00009-1>, Leicht E., Holme P., Newman M. (2006) <doi:10.1103/PhysRevE.73.026120>, Zhou T. and Zhang Y (2009) <doi:10.1140/epjb/e2009-00335-8>, and Fouss F., Pirotte A., Renders J., and Saerens M. (2007) <doi:10.1109/TKDE.2007.46>.

R functions and data sets for the work Paz, R.F., Balakrishnan, N and Bazán, J.L. (2018). L-logistic regression models: Prior sensitivity analysis, robustness to outliers and applications. Brazilian Journal of Probability and Statistics, <https://www.imstat.org/wp-content/uploads/2018/05/BJPS397.pdf>.

Conveniently generate CSS using R code.