This package provides a toolbox for R arrays. Flexibly split, bind, reshape, modify, subset and name arrays.

Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.

Prototypes for construction of a Gaussian Stochastic Process emulator (GASP) of a computer model. This is done within the objective Bayesian implementation of the GASP. The package allows for construction of a linked GASP of the composite computer model. Computational implementation follows the mathematical exposition given in publication: Ksenia N. Kyzyurova, James O. Berger, Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, (2018).<DOI:10.1137/17M1157702>.

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.

The programs were developed for estimation of parameters and testing exponential versus Pareto distribution during our work on hydrologic extremes. See Kozubowski, T.J., A.K. Panorska, F. Qeadan, and A. Gershunov (2007) <doi:10.1080/03610910802439121>, and Panorska, A.K., A. Gershunov, and T.J. Kozubowski (2007) <doi:10.1007/978-0-387-34918-3_26>.

Providing a method for Local Discrimination via Latent Class Models. The approach is described in <https://www.r-project.org/conferences/useR-2009/abstracts/pdf/Bucker.pdf>.

Allows identification of palettes derived from LTER (Long Term Ecological Research) photographs based on user criteria. Also facilitates extraction of palettes from users photos directly.

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.

Latent Class Analysis of phenotypic measurements in pedigrees and model selection based on one of two methods: likelihood-based cross-validation and Bayesian Information Criterion. Computation of individual and triplet child-parents weights in a pedigree is performed using an upward-downward algorithm. The model takes into account the familial dependence defined by the pedigree structure by considering that a class of a child depends on his parents classes via triplet-transition probabilities of the classes. The package handles the case where measurements are available on all subjects and the case where measurements are available only on symptomatic (i.e. affected) subjects. Distributions for discrete (or ordinal) and continuous data are currently implemented. The package can deal with missing data.

This package provides functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.

This package provides a classification tree method that uses Uncorrelated Linear Discriminant Analysis (ULDA) for variable selection, split determination, and model fitting in terminal nodes. It automatically handles missing values and offers visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2410.23147>.

The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.

Reads raw files from Li-COR gas analyzers and produces a dataframe that can directly be used with fluxible <https://cran.r-project.org/package=fluxible>.

An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.

Implementations of most of the existing proximity-based methods of link prediction in graphs. Among the 20 implemented methods are e.g.: Adamic L. and Adar E. (2003) <doi:10.1016/S0378-8733(03)00009-1>, Leicht E., Holme P., Newman M. (2006) <doi:10.1103/PhysRevE.73.026120>, Zhou T. and Zhang Y (2009) <doi:10.1140/epjb/e2009-00335-8>, and Fouss F., Pirotte A., Renders J., and Saerens M. (2007) <doi:10.1109/TKDE.2007.46>.

Calculates cost surfaces based on slope to be used when modelling pathways and movement potential within a landscape (Lewis, 2021) <doi:10.1007/s10816-021-09522-w>.

When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.

This package provides a fast generalized edit distance and string alignment computation mainly for linguistic aims. As a generalization to the classic edit distance algorithms, the package allows users to define custom cost for every symbol's insertion, deletion, and substitution. The package also allows character combinations in any length to be seen as a single symbol which is very useful for International Phonetic Alphabet (IPA) transcriptions with diacritics. In addition to edit distance result, users can get detailed alignment information such as all possible alignment scenarios between two strings which is useful for testing, illustration or any further usage. Either the distance matrix or its long table form can be obtained and tools to do such conversions are provided. All functions in the package are implemented in C++ and the distance matrix computation is parallelized leveraging the RcppThread package.

This package provides methods for assessing agreement between repeated measurements obtained by two or more methods using the longitudinal concordance correlation coefficient (LCC). Polynomial mixed-effects models (via nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) <doi:10.1007/s13253-018-0321-1>.

Local Polynomial Regression with Ridging.

Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach. Based on FORTRAN program ld98 implemented by Reboussin, et al. (2000) <doi:10.1016/s0197-2456(00)00057-x>.

An educational package for teaching statistics and mathematics in both primary and higher education. The objective is to assist in the teaching/learning process, both for student study planning and teacher teaching strategies. The leem package aims to provide, in a simple yet in-depth manner, knowledge of statistics and mathematics to anyone who wants to study these areas of knowledge.

Estimation of a lognormal - Generalized Pareto mixture via the Expectation-Maximization algorithm. Computation of bootstrap standard errors is supported and performed via parallel computing. Functions for random number simulation and density evaluation are also available. For more details see Bee and Santi (2025) <doi:10.48550/arXiv.2505.22507>.

This package provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. The package supports model specifications, parameter estimation, and likelihood computation, facilitating simulation and statistical inference for LBA-based experiments. For details on the LBA model, see Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.