Calculates insurance reserves and equivalence premiums using advanced numerical methods, including the Runge-Kutta algorithm and product integrals for transition probabilities. This package is useful for actuarial analyses and life insurance modeling, facilitating accurate financial projections.

In Latent Space Item Response Models, subjects and items are embedded in a multidimensional Euclidean latent space. As such, interactions among persons, items, and person-item combinations can be revealed that are unmodelled in more conventional item response theory models. This package implements the methods from Molenaar & Jeon (in press) and can be used to fit Latent Space Item Response Models to data using joint maximum likelihood estimation. The package can handle binary data, ordinal data, and data with mixed scales. The package incorporates facilities for data simulation, rotation of the latent space, and K-fold cross-validation to select the number of dimensions of the latent space.

My PhD supervisor once told me that everyone doing newspaper analysis starts by writing code to read in files from the LexisNexis newspaper archive (retrieved e.g., from <https://www.lexisnexis.com/> or any of the partner sites). However, while this is a nice exercise I do recommend, not everyone has the time. This package takes files downloaded from the newspaper archive of LexisNexis', reads them into R and offers functions for further processing.

Publication-ready regional gene locus plots similar to those produced by the web interface LocusZoom <https://my.locuszoom.org>, but running locally in R. Genetic or genomic data with gene annotation tracks are plotted via R base graphics, ggplot2 or plotly', allowing flexibility and easy customisation including laying out multiple locus plots on the same page. It uses the LDlink API <https://ldlink.nih.gov/?tab=apiaccess> to query linkage disequilibrium data from the 1000 Genomes Project and can overlay this on plots <doi:10.1093/bioadv/vbaf006>.

Data sets for Chirok Han (2024, ISBN:979-11-303-1964-3, "Lectures on Econometrics"). Students, teachers, and self-learners will find the data sets essential for replicating the results in the book.

This package provides functions to fits simple linear regression models with log normal errors and identity link, i.e. taking the responses on the original scale. See Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.

This package performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages eva <https://cran.r-project.org/package=eva>, evd <https://cran.r-project.org/package=evd>, evir <https://cran.r-project.org/package=evir>, extRemes <https://cran.r-project.org/package=extRemes>, fExtremes <https://cran.r-project.org/package=fExtremes>, ismev <https://cran.r-project.org/package=ismev>, mev <https://cran.r-project.org/package=mev>, POT <https://cran.r-project.org/package=POT> and texmex <https://cran.r-project.org/package=texmex>. Adjusted standard errors and an adjusted loglikelihood are provided, using the chandwich package <https://cran.r-project.org/package=chandwich> and the object-oriented features of the sandwich package <https://cran.r-project.org/package=sandwich>. The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification. Univariate extreme value models, including regression models, are supported.

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.

Helps to render interlinear glossed linguistic examples in html rmarkdown documents and then semi-automatically compiles the list of glosses at the end of the document. It also provides a database of linguistic glosses.

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>.

Implementation of the Swiss Confederation's standard analysis model for salary analyses <www.ebg.admin.ch/en/equal-pay-analysis-with-logib> in R. The analysis is run at company-level and the model is intended for medium-sized and large companies. It can technically be used with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis. This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities.

Assess the proportion of treatment effect explained by a longitudinal surrogate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>; and estimate the treatment effect on a longitudinal surrogate marker as described in Wang et al. (2025) <doi:10.1093/biomtc/ujaf104>. A tutorial for this package can be found at <https://www.laylaparast.com/longsurr>.

An elegant tool for processing and visualizing lipidomics data generated by mass spectrometry. LipidomicsR simplifies channel and replicate handling while providing thorough lipid species annotation. Its visualization capabilities encompass principal components analysis plots, heatmaps, volcano plots, and radar plots, enabling concise data summarization and quality assessment. Additionally, it can generate bar plots and line plots to visualize the abundance of each lipid species.

This package provides a nonparametric method to approximate Laplacian graph spectra of a network with ordered vertices. This provides a computationally efficient algorithm for obtaining an accurate and smooth estimate of the graph Laplacian basis. The approximation results can then be used for tasks like change point detection, k-sample testing, and so on. The primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).

This package performs extreme value analysis at multiple locations using functions from the evd package. Supports both point-based and gridded input data using the terra package, enabling flexible looping across spatial datasets for batch processing of generalised extreme value, Gumbel fits.

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).

Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).

Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).

Genome-wide association (GWAS) analyses of a biomarker that account for the limit of detection.

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.

Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <doi:10.1080/01621459.2020.1801448>.

Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.

Various efficient and robust bootstrap methods are implemented for linear models with least squares estimation. Functions within this package allow users to create bootstrap sampling distributions for model parameters, test hypotheses about parameters, and visualize the bootstrap sampling or null distributions. Methods implemented for linear models include the wild bootstrap by Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by Rubin (1981) <doi:10.1214/aos/1176345338>.

This package provides a wrapper around the LIBLINEAR C/C++ library for machine learning (available at <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a simple library for solving large-scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic regression, L2-loss linear SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss linear SVM and logistic regression) and L2-regularized support vector regression (with L1- or L2-loss). The main features of LiblineaR include multi-class classification (one-vs-the rest, and Crammer & Singer method), cross validation for model selection, probability estimates (logistic regression only) or weights for unbalanced data. The estimation of the models is particularly fast as compared to other libraries.