The landmark approach allows survival predictions to be updated dynamically as new measurements from an individual are recorded. The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression. To find out more about the methods in this package, please see <https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.

This package provides extensions to the leaflet package to customize legends with images, text styling, orientation, sizing, and symbology and functions to create symbols to plot on maps.

This package provides a collection of tools for the calculation of freewater metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the airâ water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models).

This package provides a simple progress bar showing estimated remaining time. Multiple forecast methods and user defined forecast method for the remaining time are supported.

Estimate the slope and intercept of a bivariate linear relationship by calculating a posterior density that is invariant to interchange and scaling of the coordinates.

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>.

This package provides a joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.

This package provides a collection of helper functions for multiple regression models fitted by lm(). Most of them are simple functions for simple tasks which can be done with coding, but may not be easy for occasional users of R. Most of the tasks addressed are those sometimes needed when using the manymome package (Cheung and Cheung, 2023, <doi:10.3758/s13428-023-02224-z>) and stdmod package (Cheung, Cheung, Lau, Hui, and Vong, 2022, <doi:10.1037/hea0001188>). However, they can also be used in other scenarios.

This package provides functions for summarizing, visualizing, and analyzing Likert-scale survey data. Includes support for computing descriptive statistics, Relative Importance Index (RII), reliability analysis (Cronbach's Alpha), and response distribution plots.

This package provides a simple mechanism to specify a symmetric block diagonal matrices (often used for covariance matrices). This is based on the domain specific language implemented in nlmixr2 but expanded to create matrices in R generally instead of specifying parts of matrices to estimate. It has expanded to include some matrix manipulation functions that are generally useful for rxode2 and nlmixr2'.

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>.

This package provides access to the LDlink API (<https://ldlink.nih.gov/?tab=apiaccess>) using the R console. This programmatic access facilitates researchers who are interested in performing batch queries in 1000 Genomes Project (2015) <doi:10.1038/nature15393> data using LDlink'. LDlink is an interactive and powerful suite of web-based tools for querying germline variants in human population groups of interest. For more details, please see Machiela et al. (2015) <doi:10.1093/bioinformatics/btv402>.

Estimate model parameters to determine whether two compounds have synergy, antagonism, or Loewe's Additivity.

Estimate haplotypic or composite pairwise linkage disequilibrium (LD) in polyploids, using either genotypes or genotype likelihoods. Support is provided to estimate the popular measures of LD: the LD coefficient D, the standardized LD coefficient D', and the Pearson correlation coefficient r. All estimates are returned with corresponding standard errors. These estimates and standard errors can then be used for shrinkage estimation. The main functions are ldfast(), ldest(), mldest(), sldest(), plot.lddf(), format_lddf(), and ldshrink(). Details of the methods are available in Gerard (2021a) <doi:10.1111/1755-0998.13349> and Gerard (2021b) <doi:10.1038/s41437-021-00462-5>.

Labels are a common construct in statistical software providing a human readable description of a variable. While variable names are succinct, quick to type, and follow a language's naming conventions, labels may be more illustrative and may use plain text and spaces. R does not provide native support for labels. Some packages, however, have made this feature available. Most notably, the Hmisc package provides labelling methods for a number of different object. Due to design decisions, these methods are not all exported, and so are unavailable for use in package development. The labelVector package supports labels for atomic vectors in a light-weight design that is suitable for use in other packages.

This package provides functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.

This package contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.

Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.

This package provides tools for fast and accurate evaluation of skew stable distributions (CDF, PDF and quantile functions), random number generation, and parameter estimation. This is libstableR as per Royuela del Val, Simmross-Wattenberg, and Alberola López (2017) <doi:10.18637/jss.v078.i01> under a new maintainer.

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>.

This package creates plots of peptides from shotgun proteomics analysis of secretome and lysate samples. These plots contain associated protein features and scores for potential secretion and truncation.

This package provides tools to create an interactive web-based visualization of a topic model that has been fit to a corpus of text data using Latent Dirichlet Allocation (LDA). Given the estimated parameters of the topic model, it computes various summary statistics as input to an interactive visualization built with D3.js that is accessed via a browser. The goal is to help users interpret the topics in their LDA topic model.

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is logConDens that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

This package performs likelihood-based inference for stationary time series extremes. The general approach follows Fawcett and Walshaw (2012) <doi:10.1002/env.2133>. Marginal extreme value inferences are adjusted for cluster dependence in the data using the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood for the model parameters. A log-likelihood for the extremal index is produced using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make inferences about extreme values. Both maximum likelihood and Bayesian approaches are available.