L1 estimation for linear regression using Barrodale and Roberts method <doi:10.1145/355616.361024> and the EM algorithm <doi:10.1023/A:1020759012226>. Estimation of mean and covariance matrix using the multivariate Laplace distribution, density, distribution function, quantile function and random number generation for univariate and multivariate Laplace distribution <doi:10.1080/03610929808832115>. Implementation of Naik and Plungpongpun <doi:10.1007/0-8176-4487-3_7> for the Generalized spatial median estimator is included.

Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.

This package provides a suite of tools for estimating, assessing model fit, simulating from, and visualizing location dependent marked point processes characterized by regularity in the pattern. You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model. ldmppr estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools. Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results. Documentation for the package ldmppr is available in the form of a vignette.

It uses phenological and productivity-related variables derived from time series of vegetation indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description of the methods used in the package to calculate the indicator.

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>.

Runtime for serving containers that can execute R code on the AWS Lambda serverless compute service <https://aws.amazon.com/lambda/>. Provides the necessary functionality for handling the various endpoints required for accepting new input and sending responses.

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>.

Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the Python library PyLMD'.

Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>, O'Connor (1999) <doi:10.3758/BF03200753>, Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and Wampold (1995, ISBN:0-89391-919-5).

This package produces a group screening procedure that is based on maximum Lq-likelihood estimation, to simultaneously account for the group structure and data contamination in variable screening. The methods are described in Li, Y., Li, R., Qin, Y., Lin, C., & Yang, Y. (2021) Robust Group Variable Screening Based on Maximum Lq-likelihood Estimation. Statistics in Medicine, 40:6818-6834.<doi:10.1002/sim.9212>.

Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 <doi:10.1139/f06-039>), focused on the marine environment.

Automatically install, update, and load CRAN', GitHub', and Bioconductor packages in a single function call. By accepting bare unquoted names for packages, it's easy to add or remove packages from the list.

This package provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts. As of now, two Portuguese lexicons are available: SentiLex-PT02 and OpLexicon (v2.1 and v3.0).

Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical methods rely on strong assumptions such as the exclusion criterion, which states that instrumental effects must be entirely mediated by treatments. In the so-called "leaky" IV setting, candidate instruments are allowed to have some direct influence on outcomes, rendering the average treatment effect (ATE) unidentifiable. But with limits on the amount of information leakage, we may still recover sharp bounds on the ATE, providing partial identification. This package implements methods for ATE bounding in the leaky IV setting with linear structural equations. For details, see Watson et al. (2024) <doi:10.48550/arXiv.2404.04446>.

The leader clustering algorithm provides a means for clustering a set of data points. Unlike many other clustering algorithms it does not require the user to specify the number of clusters, but instead requires the approximate radius of a cluster as its primary tuning parameter. The package provides a fast implementation of this algorithm in n-dimensions using Lp-distances (with special cases for p=1,2, and infinity) as well as for spatial data using the Haversine formula, which takes latitude/longitude pairs as inputs and clusters based on great circle distances.

Create maps made of lines. The package contains one function: linemap(). linemap() displays a map made of lines using a raster or gridded data.

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.

This package provides a bootstrap proportion test for Brand Lift Testing to quantify the effectiveness of online advertising. Methods of the bootstrap proportion test are presented in Liu, Yu, Mao, Wu, Dyer (2023) <doi:10.1145/3583780.3615021>.

This package provides functions for regional frequency analysis using the methods of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis: an approach based on L-moments".

This package provides a high level interface for torch providing utilities to reduce the the amount of code needed for common tasks, abstract away torch details and make the same code work on both the CPU and GPU'. It's flexible enough to support expressing a large range of models. It's heavily inspired by fastai by Howard et al. (2020) <doi:10.48550/arXiv.2002.04688>, Keras by Chollet et al. (2015) and PyTorch Lightning by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.

Supplies a LazyData facility for packages which have data sets but do not provide LazyData: true. A single function is is included, requireData, which is a drop-in replacement for base::require, but carrying the additional functionality. By default, it suppresses package startup messages as well. See argument reallyQuitely'.

Bandwidth selection for kernel density estimators of 2-d level sets and highest density regions. It applies a plug-in strategy to estimate the asymptotic risk function and minimize to get the optimal bandwidth matrix. See Doss and Weng (2018) <arXiv:1806.00731> for more detail.

This package implements Cumulative Sum (CUSUM) control charts specifically designed for monitoring processes following a Gamma distribution. Provides functions to estimate distribution parameters, simulate control limits, and apply cautious learning schemes for adaptive thresholding. It supports upward and downward monitoring with guaranteed performance evaluated via Monte Carlo simulations. It is useful for quality control applications in industries where data follows a Gamma distribution. Methods are based on Madrid-Alvarez et al. (2024) <doi:10.1002/qre.3464> and Madrid-Alvarez et al. (2024) <doi:10.1080/08982112.2024.2440368>.

This package performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.