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>.

This package provides methods of developing linear time series modelling. Methods are given for loglikelihood computation, forecasting and simulation.

Due to lack of proper inference procedure and software, the ordinary linear regression model is seldom used in practice for the analysis of right censored data. This paper presents an S-Plus/R program that implements a recently developed inference procedure (Jin, Lin and Ying, 2006) <doi:10.1093/biomet/93.1.147> for the accelerated failure time model based on the least-squares principle.

Spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal, multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.

Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.

This package performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) <doi:10.1080/01621459.1982.10477898>. The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) <doi:10.1002/pst.2069>. The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures.

This package provides functions to upload vectorial data and derive landscape connectivity metrics in habitat or matrix systems. Additionally, includes an approach to assess individual patch contribution to the overall landscape connectivity, enabling the prioritization of habitat patches. The computation of landscape connectivity and patch importance are very useful in Landscape Ecology research. The metrics available are: number of components, number of links, size of the largest component, mean size of components, class coincidence probability, landscape coincidence probability, characteristic path length, expected cluster size, area-weighted flux and integral index of connectivity. Pascual-Hortal, L., and Saura, S. (2006) <doi:10.1007/s10980-006-0013-z> Urban, D., and Keitt, T. (2001) <doi:10.2307/2679983> Laita, A., Kotiaho, J., Monkkonen, M. (2011) <doi:10.1007/s10980-011-9620-4>.

Impute observed values below the limit of detection (LOD) via censored likelihood multiple imputation (CLMI) in single-pollutant models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>. CLMI handles exposure detection limits that may change throughout the course of exposure assessment. lodi provides functions for imputing and pooling for this method.

This package provides a unified latent class modeling framework that encompasses both latent class analysis (LCA) and latent profile analysis (LPA), offering a one-stop solution for latent class modeling. It implements state-of-the-art parameter estimation methods, including the expectationâ maximization (EM) algorithm, neural network estimation (NNE; requires users to have Python and its dependent libraries installed on their computer), and integration with Mplus (requires users to have Mplus installed on their computer). In addition, it provides commonly used model fit indices such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as classification accuracy measures such as entropy. The package also includes fully functional likelihood ratio tests (LRT) and bootstrap likelihood ratio tests (BLRT) to facilitate model comparison, along with bootstrap-based and observed information matrix-based standard error estimation. Furthermore, it supports the standard three-step approach for LCA, LPA, and latent transition analysis (LTA) with covariates, enabling detailed covariate analysis. Finally, it includes several user-friendly auxiliary functions to enhance interactive usability.

This package provides functions for regional frequency analysis using the methods of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis: an approach based on L-moments".

Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.

Data sets exemplifying statistical methods, and some facilitatory utility functions used in ``Analyzing Linguistic Data: A practical introduction to statistics using R'', Cambridge University Press, 2008.

This package provides a bootstrap proportion test for Brand Lift Testing to quantify the effectiveness of online advertising. Methods of the bootstrap proportion test are presented in Liu, Yu, Mao, Wu, Dyer (2023) <doi:10.1145/3583780.3615021>.

This package performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.

Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

This package provides a static library for Imath (see <https://github.com/AcademySoftwareFoundation/Imath>), a library for functions and data types common in computer graphics applications, including a 16-bit floating-point type.

Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc. REFERENCES i. Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>, ii. Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006>, iii. Imdadullah, Aslam, and Saima (2017), iv. Marquardt (1970) <doi:10.2307/1267205>.

This package contains 128 palettes from Color Lisa. All palettes are based on masterpieces from the worlds greatest artists. For more information, see <http://colorlisa.com/>.

Location and scale hypothesis testing using the LePage test and variants of its as proposed by Hussain A. and Tsagris M. (2025), <doi:10.48550/arXiv.2509.19126>.

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.

Estimate linear quantile mixtures based on Time-Constant (TC) and/or Time-Varying (TV), discrete, random coefficients.

Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.

Efficient implementation of Friedman's boosting algorithm with l2-loss function and coordinate direction (design matrix columns) basis functions.