Wrapper functions for the implementation of lagged weighted quantile sum regression, as per Gennings et al (2020) <doi:10.1016/j.envres.2020.109529>.

This package provides methods for estimating borders of uniform distribution on the interval (one-dimensional) and on the elliptical domain (two-dimensional) under measurement errors. For one-dimensional case, it also estimates the length of underlying uniform domain and tests the hypothesized length against two-sided or one-sided alternatives. For two-dimensional case, it estimates the area of underlying uniform domain. It works with numerical inputs as well as with pictures in JPG format.

Translates R help documentation on the fly by using a Large Language model of your choice. If you are using RStudio or Positron the translated help will appear in the help pane.

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.

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>.

This package provides methods for estimation and statistical inference on directional and fluctuating selection in age-structured populations.

Software for computing a log-concave (maximum likelihood) estimator for independent and identically distributed data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).

This package provides the OpenEXR static library and C++ headers for high-dynamic-range image I/O (see <https://openexr.com/>) needed to link R packages against the OpenEXR library, along with a basic R interface to load EXR images.

This package provides tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.

Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering. See Einbeck, Tutz and Evers (2005) <doi:10.1007/s11222-005-4073-8> and Ameijeiras-Alonso and Einbeck (2023) <doi:10.1007/s11634-023-00575-1>.

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.

Automatically install, update, and load CRAN', GitHub', and Bioconductor packages in a single function call. By accepting bare unquoted names for packages, it's easy to add or remove packages from the list.

Conducts a cointegration test for high-dimensional vector autoregressions (VARs) of order k based on the large N,T asymptotics of Bykhovskaya and Gorin, 2022 (<doi:10.48550/arXiv.2202.07150>). The implemented test is a modification of the Johansen likelihood ratio test. In the absence of cointegration the test converges to the partial sum of the Airy-1 point process. This package contains simulated quantiles of the first ten partial sums of the Airy-1 point process that are precise up to the first three digits.

This package provides an interface to the financial data platform <https://datahub.limex.com/>., enabling users to retrieve real-time and historical financial data. Functions within the package allow access to instruments, candlestick charts, fundamentals, news, events, models, and trading signals. Authentication is managed through user-specific API tokens, which are securely handled via environment variables.

An updated implementation of R package ranger by Wright et al, (2017) <doi:10.18637/jss.v077.i01> for training and predicting from random forests, particularly suited to high-dimensional data, and for embedding in Multiple Imputation by Chained Equations (MICE) by van Buuren (2007) <doi:10.1177/0962280206074463>. Ensembles of classification and regression trees are currently supported. Sparse data of class dgCMatrix (R package Matrix') can be directly analyzed. Conventional bagged predictions are available alongside an efficient prediction for MICE via the algorithm proposed by Doove et al (2014) <doi:10.1016/j.csda.2013.10.025>. Trained forests can be written to and read from storage. Survival and probability forests are not supported in the update, nor is data of class gwaa.data (R package GenABEL'); use the original ranger package for these analyses.

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

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

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>.

Principal component analysis (PCA) is one of the most widely used data analysis techniques. This package provides a series of vignettes explaining PCA starting from basic concepts. The primary purpose is to serve as a self-study resource for anyone wishing to understand PCA better. A few convenience functions are provided as well.

For fitting Bayesian joint latent class and regression models using Gibbs sampling. See the documentation for the model. The technical details of the model implemented here are described in Elliott, Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham, Belinda L., "Methods to account for uncertainty in latent class assignments when using latent classes as predictors in regression models, with application to acculturation strategy measures" (2020) In press at Epidemiology <doi:10.1097/EDE.0000000000001139>.

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.

Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) <doi:10.1093/biomet/asx036>, interleaved lattice-based maximin distance designs proposed in Xu He (2018) <doi:10.1093/biomet/asy069>, interleaved lattice-based designs with low fill and high separation distance properties proposed in Xu He (2024) <doi:10.1137/23M156940X>, (sliced) rotated sphere packing designs proposed in Xu He (2017) <doi:10.1080/01621459.2016.1222289> and Xu He (2019) <doi:10.1080/00401706.2018.1458655>, densest packing-based maximum projections designs proposed in Xu He (2020) <doi:10.1093/biomet/asaa057> and Xu He (2018) <doi:10.48550/arXiv.1709.02062>, maximin distance designs for mixed continuous, ordinal, and binary variables proposed in Hui Lan and Xu He (2025) <doi:10.48550/arXiv.2507.23405>, and optimized and regularly repeated lattice-based Latin hypercube designs for large-scale computer experiments proposed in Xu He, Junpeng Gong, and Zhaohui Li (2025) <doi:10.48550/arXiv.2506.04582>.

Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) <arXiv:2005.13011>.

Local partial likelihood estimation by Fan, Lin and Zhou(2006)<doi:10.1214/009053605000000796> and simultaneous confidence band is a set of tools to test the covariates-biomarker interaction for survival data. Test for the covariates-biomarker interaction using the bootstrap method and the asymptotic method with simultaneous confidence band (Liu, Jiang and Chen (2015)<doi:10.1002/sim.6563>).