Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.

Extracts zip files, converts Word', Excel', and html'/'htm files to pdf format. Word and Excel conversion uses VBScript', while html'/'htm conversion uses webshot and PhantomJS'. Additionally, the package merges pdf files into a single document. This package is only supported on Windows due to VBScript dependencies.

Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. LKT is a method to compute features of student data that are used as predictors of subsequent performance. LKT allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of LiblineaR', which enables extremely fast solutions compared to base glm() in R.

This package provides a LaTeX Letter class for rmarkdown', using the pandoc-letter template adapted for use with markdown'.

Flexible functions that use lme4 as computational engine for fitting models used in Genomic Selection (GS). GS is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. The lme4GS package is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance-covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data. For more details see Caamal-Pat et.al. (2021) <doi:10.3389/fgene.2021.680569>.

This package provides tools are provided to expand vectors of short URLs into long URLs'. No API services are used, which may mean that this operates more slowly than API services do (since they usually cache results of expansions that every user of the service requests). You can setup your own caching layer with the memoise package if you wish to have a speedup during single sessions or add larger dependencies, such as Redis', to gain a longer-term performance boost at the expense of added complexity.

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

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>.

Handling, processing, and analyzing geographic data on species distributions and environmental variables. Read Vilela & Villalobos (2015) <doi:10.1111/2041-210X.12401> for details.

This package provides a graphical user interface with an integrated diagrammer for latent variable models from the lavaan package. It offers two core functions: first, lavaangui() launches a web application that allows users to specify models by drawing path diagrams, fitting them, assessing model fit, and more; second, plot_lavaan() creates interactive path diagrams from models specified in lavaan'. After customizing a diagram interactively, export_plot() saves it to a file, enabling reproducible scripts without sacrificing fine-grained control over appearance. Karch (2024) <doi: 10.1080/10705511.2024.2420678> contains a tutorial.

This package provides functions for estimating the gliding box lacunarity (GBL), covariance, and pair-correlation of a random closed set (RACS) in 2D from a binary coverage map (e.g. presence-absence land cover maps). Contains a number of newly-developed covariance-based estimators of GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and balanced estimators, proposed by Picka (2000) <http://www.jstor.org/stable/1428408>, for covariance, centred covariance, and pair-correlation. Also contains methods for estimating contagion-like properties of RACS and simulating 2D Boolean models. Binary coverage maps are usually represented as raster images with pixel values of TRUE, FALSE or NA, with NA representing unobserved pixels. A demo for extracting such a binary map from a geospatial data format is provided. Binary maps may also be represented using polygonal sets as the foreground, however for most computations such maps are converted into raster images. The package is based on research conducted during the author's PhD studies.

This package creates plots of peptides from shotgun proteomics analysis of secretome and lysate samples. These plots contain associated protein features and scores for potential secretion and truncation.

An extendable toolkit for interactive data visualization and exploration.

Tests whether the linear hypothesis of a model is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation Specification Error Test) test is implemented and Wald tests can be carried out. Although RESET test is widely used to test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel approach that generalizes well known specification tests such as Ramsey's. This test relies on wild-bootstrap; this package implements this approach to be usable with any function that fits linear models and is compatible with the update() function such as stats'::lm(), lfe'::felm() and forecast'::Arima(), for ARMA (autoregressiveâ moving-average) models. Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov Smirnov, described by the authors, and custom distributions such as Mammen (discrete and continuous) and Rademacher. Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>.

Estimation of latent class models with individual covariates for capture-recapture data. See Bartolucci, F. and Forcina, A. (2022), Estimating the size of a closed population by modeling latent and observed heterogeneity, Biometrics, 80(2), ujae017.

This package provides tools for assessing equivalence of similar Logistic Regression models.

Local Polynomial Regression with Ridging.

Download Internet Protocol (IP) address location and more from the ip-api application programming interface (API) <https://ip-api.com/>. The package makes it easy to get the latitude, longitude, country, region, and organisation associated to the provided IP address. The information is conveniently returned in a rectangular format.

This package provides regularized structural equation modeling (regularized SEM) with non-smooth penalty functions (e.g., lasso) building on lavaan'. The package is heavily inspired by the ['regsem'](<https://github.com/Rjacobucci/regsem>) and ['lslx'](<https://github.com/psyphh/lslx>) packages.

Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) <doi:10.31234/osf.io/dy79z>.

Automatic detection of hyperlinks for packages and calls in the text of rmarkdown or quarto documents.

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.

Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. Feedback loops are paths from a node to itself without visiting any other node twice, and they have important regulatory functions. Loops are reported with their order of participating nodes and their length, and whether the loop is a positive or a negative feedback loop. An upper limit of the number of feedback loops limits runtime (which scales with feedback loop count). Model parametrizations and values of the modelled variables are accounted for. Computation uses the characteristics of the Jacobian matrix as described e.g. in Thomas and Kaufman (2002) <doi:10.1016/s1631-0691(02)01452-x>. Input can be the Jacobian matrix of the ODE model or the ODE function definition; in the latter case, the Jacobian matrix is determined using numDeriv'. Graph-based algorithms from igraph are employed for path detection.

Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elà as, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.