LP nonparametric high-dimensional K-sample comparison method that includes (i) confirmatory test, (ii) exploratory analysis, and (iii) options to output a data-driven LP-transformed matrix for classification. The primary reference is Mukhopadhyay, S. and Wang, K. (2020, Biometrika); <arXiv:1810.01724>.

Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.

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

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

Provide access to the lz-string <http://pieroxy.net/blog/pages/lz-string/index.html> C++ library for Lempel-Ziv (LZ) based compression and decompression of strings.

This package provides classes and methods for spatially explicit land use change modelling in R.

This package provides utilities to detect common data leakage patterns including train/test contamination, temporal leakage, and data duplication, enhancing model reliability and reproducibility in machine learning workflows. Generates diagnostic reports and visual summaries to support data validation. Methods based on best practices from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0387848570).

An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.

Bayesian model averaging (BMA) algorithms for univariate link latent Gaussian models (ULLGMs). For detailed information, refer to Steel M.F.J. & Zens G. (2024) "Model Uncertainty in Latent Gaussian Models with Univariate Link Function" <doi:10.48550/arXiv.2406.17318>. The package supports various g-priors and a beta-binomial prior on the model space. It also includes auxiliary functions for visualizing and tabulating BMA results. Currently, it offers an out-of-the-box solution for model averaging of Poisson log-normal (PLN) and binomial logistic-normal (BiL) models. The codebase is designed to be easily extendable to other likelihoods, priors, and link functions.

Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options. The original package description was published as Goslee (2011) <doi:10.18637/jss.v043.i04>, and details of the topographic corrections in Goslee (2012) <doi:10.14358/PERS.78.9.973>.

An interface for the image processing program ImageJ', which allows a rapid digital image analysis for particle sizes. This package includes function to write an ImageJ macro which is optimized for a leaf area analysis by default.

Simulate expected equilibrium length composition, yield-per-recruit, and the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR model to length data to estimate selectivity, relative apical fishing mortality, and the spawning potential ratio for data-limited fisheries. See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the LBSPR assessment method.

Implementations of estimation algorithm of low rank plus sparse structured VAR model by using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). It relates to the algorithm in Sumanta, Li, and Michailidis (2019) <doi:10.1109/TSP.2018.2887401>.

Miscellaneous scripts, e.g. functionality to make and plot factor diagrams for the statistical design.

Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L].

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>).

Real-time quantitative polymerase chain reaction (qPCR) data sets by Lievens et al. (2012) <doi:10.1093/nar/gkr775>. Provides one single tabular tidy data set in long format, encompassing three dilution series, targeted against the soybean Lectin endogene. Each dilution series was assayed in one of the following PCR-efficiency-modifying conditions: no PCR inhibition, inhibition by isopropanol and inhibition by tannic acid. The inhibitors were co-diluted along with the dilution series. The co-dilution series consists of a five-point, five-fold serial dilution. For each concentration there are 18 replicates. Each amplification curve is 60 cycles long. Original raw data file is available at the Supplementary Data section at Nucleic Acids Research Online <doi:10.1093/nar/gkr775>.

Gives users seeking federal lobbying disclosures an easier way to query the API maintained by the Senate federal lobbying disclosures database <https://lda.senate.gov/api/redoc/v1/> to find out how much companies and other entities are spending to lobby Congress and the federal government. It allows for search terms such as keywords, time periods and entity names. It then attempts to clean, or at least flag, filings that could provide incorrect results when seeking to answer the question: How much is being spent on lobbying our Congress and the administration and what issues do they care about?

Miscellaneous functions commonly used by LuLab. This package aims to help more researchers on epidemiology to perform data management and visualization more efficiently.

This package provides tools to retrieve and summarize taxonomic information and synonymy data for reptile species using data scraped from The Reptile Database website (<https://reptile-database.reptarium.cz/>). Outputs include clean and structured data frames useful for ecological, evolutionary, and conservation research.

This package provides a comprehensive analysis tool for metabolomics data. It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton <http://www.metabolomics-forum.com/index.php?topic=281.0> Jeff Xia <https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R> Travers Ching, Xun Zhu, Lana X. Garmire (2018) <doi:10.1371/journal.pcbi.1006076>.

Fits look-up tables by filling entries with the mean or median values of observations fall in partitions of the feature space. Partitions can be determined by user of the package using input argument feature.boundaries, and dimensions of the feature space can be any combination of continuous and categorical features provided by the data set. A Predict function directly fetches corresponding entry value, and a default value is defined as the mean or median of all available observations. The table and other components are represented using the S4 class lookupTable.

Temporary and permanent message queues for R. Built on top of SQLite databases. SQLite provides locking, and makes it possible to detect crashed consumers. Crashed jobs can be automatically marked as "failed", or put in the queue again, potentially a limited number of times.

An implementation of locally Gaussian distributions. It provides methods for implementing locally Gaussian multivariate density estimation, conditional density estimation, various independence tests for iid and time series data, a test for conditional independence and a test for financial contagion.