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 a lightweight interchange layer for single-cell and spatial omics data, built on the L-star model of labelled axes and typed fields over them, serialized to the Zarr format. Provides bidirectional converters ("profiles") for Seurat', SingleCellExperiment', Conos', and pagoda2 objects, including collections of heterogeneous samples, via a shared C++ core ('libstar') so the same store is readable from R, Python', and C++.

An (upper) level set of a function is the set of inputs for which the function value is at or above a specified threshold. (Also called an excursion set). Applications of level sets include confidence or credible regions for parameters of statistical models, where the function is the likelihood or posterior density; regions where classification rules assign high probability to a given class; and scientific or engineering models where one is interested in input regions for which model output is above a threshold. This package maps out the boundary of a level set by finding its intersections with collections of 1-dimensional rays, generalizing a proposal by Kim and Lindsay (Statistica Sinica 21:923-948, 2011). Tools are provided to generate rays, find intersections, and visualize results. The package makes few assumptions about the studied function: it may be discontinuous, it may have a complicated feasible region, and the target level set may be non-convex or have multiple, disconnected parts. Vignettes describe package usage and show examples with two to five input space dimensions.

New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a "reference class" and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://hdl.handle.net/10393/34889>.

Data used as examples in the loon package.

Logic Forest is an ensemble machine learning method that identifies important and interpretable combinations of binary predictors using logic regression trees to model complex relationships with an outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.

This package provides tools for creating and using lenses to simplify data manipulation. Lenses are composable getter/setter pairs for working with data in a purely functional way. Inspired by the Haskell library lens (Kmett, 2012) <https://hackage.haskell.org/package/lens>. For a fairly comprehensive (and highly technical) history of lenses please see the lens wiki <https://github.com/ekmett/lens/wiki/History-of-Lenses>.

The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.

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.

This package provides in built datasets and three functions. These functions are mobility_index, nonStanTest and linkedLives. The mobility_index function facilitates the calculation of lifecourse fluidity, whilst the nonStanTest and the linkedLives functions allow the user to determine the probability that the observed sequence data was due to chance. The linkedLives function acknowledges the fact that some individuals may have identical sequences. The datasets available provide sequence data on marital status(maritalData) and mobility (mydata) for a selected group of individuals from the British Household Panel Study (BHPS). In addition, personal and house ID's for 100 individuals are provided in a third dataset (myHouseID) from the BHPS.

Data sets on various litter types like beach litter, riverain litter, floating litter, and seafloor litter are rapidly growing. This package offers a simple user interface to analyse these litter data in a consistent and reproducible way. It also provides functions to facilitate several kinds of litter analysis, e.g., trend analysis, power analysis, and baseline analysis. Under the hood, these functions are also used by the user interface. See Schulz et al. (2019) <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are advised to run litteR in RStudio'. See our vignette: Installation manual for RStudio and litteR'.

Simplifies the whole process of creating stacked tilted maps, that are often used in scientific publications to show different environmental layers for a geographical region. Tilting maps and layering them allows to easily draw visual correlations between these environmental layers.

This package provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.

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>.

Supervised classification methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to test them on, which highlight the strengths and weaknesses of each technique.

Input latitude and longitude values or an sf/sfc POINT object and get back the time zone in which they exist. Two methods are implemented. One is very fast and uses Rcpp in conjunction with data from the Javascript library (<https://github.com/darkskyapp/tz-lookup-oss/>). This method also works outside of countries borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the sf package to intersect points with a detailed map of time zones from here: <https://github.com/evansiroky/timezone-boundary-builder/>. The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect.

Designed to query Longitudinal Employer-Household Dynamics (LEHD) workplace/residential association and origin-destination flat files and optionally aggregate Census block-level data to block group, tract, county, or state. Data comes from the LODES FTP server <https://lehd.ces.census.gov/data/lodes/LODES8/>.

Automated analysis and modeling of longitudinal omics data (e.g. breath metabolomics') using generalized spline mixed effect models. Including automated filtering of noise parameters and determination of breakpoints.

This is a Neural Network regression model implementation using Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.

Collect marketing data from LinkedIn Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

Inference for the Lorenz and penalized Lorenz regressions. More broadly, the package proposes functions to assess inequality and graphically represent it. The Lorenz Regression procedure is introduced in Heuchenne and Jacquemain (2022) <doi:10.1016/j.csda.2021.107347> and in Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024) <doi:10.1214/23-EJS2200>.

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 to calculate lunar and other related environmental covariates.

This package provides functions to fit log-multiplicative models using gnm', with support for convenient printing, plots, and jackknife/bootstrap standard errors. For complex survey data, models can be fitted from design objects from the survey package. Currently supported models include UNIDIFF (Erikson & Goldthorpe, 1992), a.k.a. log-multiplicative layer effect model (Xie, 1992) <doi:10.2307/2096242>, and several association models: Goodman (1979) <doi:10.2307/2286971> row-column association models of the RC(M) and RC(M)-L families with one or several dimensions; two skew-symmetric association models proposed by Yamaguchi (1990) <doi:10.2307/271086> and by van der Heijden & Mooijaart (1995) <doi:10.1177/0049124195024001002> Functions allow computing the intrinsic association coefficient (see Bouchet-Valat (2022) <doi:10.1177/0049124119852389>) and the Altham (1970) index <doi:10.1111/j.2517-6161.1970.tb00816.x>, including via the Bayes shrinkage estimator proposed by Zhou (2015) <doi:10.1177/0081175015570097>; and the RAS/IPF/Deming-Stephan algorithm.