This package provides tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).

This package provides list-processing utilities inspired by the SRFI-1 list library for Scheme (<https://srfi.schemers.org/srfi-1/srfi-1.html>), including car/cdr family accessors, zip, pairwise, for.each, pair.fold.right and friends. Higher-order helpers that are orthogonal to list processing are deferred to the functional package; this package is freely a mixture of implementation and API.

This package provides a way to fit Parsimonious Finite Mixtures of Multivariate Elliptical Leptokurtic-Normals. Two methods of estimation are implemented.

This package provides a flexible and easy-to use interface for the soil vegetation atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran. The model simulates daily transpiration, interception, soil and snow evaporation, streamflow and soil water fluxes through a soil profile covered with vegetation, as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) <doi:10.1175/1525-7541(2003)004%3C1276:SOAETS%3E2.0.CO;2>. A set of high-level functions for model set up, execution and parallelization provides easy access to plot-level SVAT simulations, as well as multi-run and large-scale applications.

Generate a local library copy with relevant packages. All packages currently found within the search path - except base packages - will be copied to the directory provided and can be used later on with the .libPaths() function.

It implements Expectation/Conditional Maximization Either (ECME) and rapidly converging algorithms as well as Bayesian inference for linear mixed models, which is described in Schafer, J.L. (1998) "Some improved procedures for linear mixed models". Dept. of Statistics, The Pennsylvania State University.

LimeSurvey is Free/Libre Open Source Software for the development and administrations of online studies, using sophisticated tailoring capabilities to support multiple study designs (see <https://www.limesurvey.org>). This package supports programmatic creation of surveys that can then be imported into LimeSurvey', as well as user friendly import of responses from LimeSurvey studies.

Bootstrap routines for nested linear mixed effects models fit using either lme4 or nlme'. The provided bootstrap() function implements the parametric, residual, cases, random effect block (REB), and wild bootstrap procedures. An overview of these procedures can be found in Van der Leeden et al. (2008) <doi: 10.1007/978-0-387-73186-5_11>, Carpenter, Goldstein & Rasbash (2003) <doi: 10.1111/1467-9876.00415>, and Chambers & Chandra (2013) <doi: 10.1080/10618600.2012.681216>.

Lipid Maps Rest service. Researchers can access the Lipid Maps Rest service programmatically and conveniently integrate it into the current workflow or packages.

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>.

This package implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis.

This package provides an interactive shiny application to construct stage-structured life tables from tabular input data. The application includes input validation, demographic calculations, visualization tools, and export of tables and figures to support reproducible workflows in ecological and entomological studies. Methods for life table construction follow classical demographic approaches described in Martinez (2015) <doi:10.13140/RG.2.2.21333.65760>.

This package provides methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others). SUR (for parameter estimation), logit'/'probit (for binary classification), and VARMA (for time-series forecasting) are implemented. Evaluations are both in-sample and out-of-sample. It is designed to be efficient in terms of CPU usage and memory consumption.

Computes the Lomb-Scargle Periodogram and actogram for evenly or unevenly sampled time series. Includes a randomization procedure to obtain exact p-values. Partially based on C original by Press et al. (Numerical Recipes) and the Python module Astropy. For more information see Ruf, T. (1999). The Lomb-Scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biological Rhythm Research, 30(2), 178-201.

Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.

This package creates a consensus genetic map by merging linkage maps from different populations. The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps. This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved. When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.

Estimation of a multi-group count regression models (i.e., Poisson, negative binomial) with latent covariates. This packages provides two extensions compared to ordinary count regression models based on a generalized linear model: First, measurement models for the predictors can be specified allowing to account for measurement error. Second, the count regression can be simultaneously estimated in multiple groups with stochastic group weights. The marginal maximum likelihood estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.

Converts table-like objects to stand-alone PDF or PNG. Can be used to embed tables and arbitrary content in PDF or Word documents. Provides a low-level R interface for creating LaTeX code, e.g. command() and a high-level interface for creating PDF documents, e.g. as.pdf.data.frame(). Extensive customization is available via mid-level functions, e.g. as.tabular(). See also package?latexpdf'. Support for PNG is experimental; see as.png.data.frame'. Adapted from metrumrg <https://r-forge.r-project.org/R/?group_id=1215>. Requires a compatible installation of pdflatex', e.g. <https://miktex.org/>.

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

This package provides a joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.

R interface for working with nanometer scale secondary ion mass spectrometry (NanoSIMS) data exported from Look at NanoSIMS.

Calculates 3D lacunarity from voxel data. It is designed for use with point clouds generated from Light Detection And Ranging (LiDAR) scans in order to measure the spatial heterogeneity of 3-dimensional structures such as forest stands. It provides fast C++ functions to efficiently bin point cloud data into voxels and calculate lacunarity using different variants of the gliding-box algorithm originated by Allain & Cloitre (1991) <doi:10.1103/PhysRevA.44.3552>.

Repairs malformed JSON strings, particularly those generated by Large Language Models. Handles missing quotes, trailing commas, unquoted keys, and other common JSON syntax errors.

The reference implementation of model equations and default parameters for the toxicokinetic-toxicodynamic (TKTD) model of the Lemna (duckweed) aquatic plant. Lemna is a standard test macrophyte used in ecotox effect studies. The model was described and published by the SETAC Europe Interest Group Effect Modeling. It is a refined description of the Lemna TKTD model published by Schmitt et al. (2013) <doi:10.1016/j.ecolmodel.2013.01.017>.