This package provides a novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.

The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.

Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015)<doi: 10.1007/s00180-015-0567-9> and Mai Zhou and Yifan Yang (2017) <doi: 10.1002/wics.1400>. More applications could be found in Mai Zhou (2015) <doi: 10.1201/b18598>.

Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) <doi:10.1111/2041-210X.12141> in a few user-friendly R functions. All functions handle data obtained with Ahmes 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes.

The proposed method aims at predicting the longitudinal mean response trajectory by a kernel-based estimator. The kernel estimator is constructed by imposing weights based on subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. Users could also perform variable selections to derive functional predictors with predictive significance by the proposed multiplicative model with multivariate Gaussian kernels.

Lake morphometry metrics are used by limnologists to understand, among other things, the ecological processes in a lake. Traditionally, these metrics are calculated by hand, with planimeters, and increasingly with commercial GIS products. All of these methods work; however, they are either outdated, difficult to reproduce, or require expensive licenses to use. The lakemorpho package provides the tools to calculate a typical suite of these metrics from an input elevation model and lake polygon. The metrics currently supported are: fetch, major axis, minor axis, major/minor axis ratio, maximum length, maximum width, mean width, maximum depth, mean depth, shoreline development, shoreline length, surface area, and volume.

This package provides a bioinformatics pipeline for performing taxonomic assignment of DNA metabarcoding sequence data while considering geographic location. A detailed tutorial is available at <https://urodelan.github.io/Local_Taxa_Tool_Tutorial/>. A manuscript describing these methods is in preparation.

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 the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.

An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.

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.

An R implementation of the LexRank algorithm described by G. Erkan and D. R. Radev (2004) <DOI:10.1613/jair.1523>.

Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) <doi:10.1016/j.jmva.2019.04.004>.

This package provides tools to decompose differences in cohort health expectancy (HE) by age and cause using longitudinal data. The package implements a novel longitudinal attribution method based on a semiparametric additive hazards model with time-dependent covariates, specifically designed to address interval censoring and semi-competing risks via a copula framework. The resulting age-cause-specific contributions to disability prevalence and death probability can be used to quantify and decompose differences in cohort HE between groups. The package supports stepwise replacement decomposition algorithms and is applicable to cohort-based health disparity research across diverse populations. Related methods include Sun et al. (2023) <doi:10.1177/09622802221133552>.

Bootstrap routines for nested linear mixed effects models fit using either lme4 or nlme'. The provided bootstrap() function implements the parametric, residual, cases, random effect block (REB), and wild bootstrap procedures. An overview of these procedures can be found in Van der Leeden et al. (2008) <doi: 10.1007/978-0-387-73186-5_11>, Carpenter, Goldstein & Rasbash (2003) <doi: 10.1111/1467-9876.00415>, and Chambers & Chandra (2013) <doi: 10.1080/10618600.2012.681216>.

Studies that report shifts in species distributions may be biased by the shape of the study area. The main functionality of this package is to calculate the Latitudinal Bias Index (LBI) for any given shape. The LBI is bounded between +1 (100% probability to exclusively record latitudinal shifts, i.e., range shifts data sampled along a perfectly South-North oriented straight line) and -1 (100% probability to exclusively record longitudinal shifts, i.e., range shifts data sampled along a perfectly East-West oriented straight line).

This package provides an extension to factors called lfactor that are similar to factors but allows users to refer to lfactor levels by either the level or the label.

Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes. The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.

R interface for working with nanometer scale secondary ion mass spectrometry (NanoSIMS) data exported from Look at NanoSIMS.

Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).

This package provides a collection of tools for interactive manipulation of (spatial) data layers on leaflet web maps. Tools include editing of existing layers, creation of new layers through drawing of shapes (points, lines, polygons), deletion of shapes as well as cutting holes into existing shapes. Provides control over options to e.g. prevent self-intersection of polygons and lines or to enable/disable snapping to align shapes.

Time series analysis based on lambda transformer and variational seq2seq, built on Torch'.

Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use.

It uses phenological and productivity-related variables derived from time series of vegetation indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description of the methods used in the package to calculate the indicator.