Fit different model forms to single-cohort litter decomposition data (mass remaining through time) using likelihood-based estimation. Models span simple empirical to process-motivated forms with differing numbers of free parameters. Provides parameter estimates, uncertainty, and tools for model comparison/selection. Based on Cornwell & Weedon (2013) <doi:10.1111/2041-210X.12138>.

This package provides a class that links matrix-like objects (nodes) by rows or by columns while behaving similarly to a base R matrix. Very large matrices are supported if the nodes are file-backed matrices.

Extends the functionality of the tourr package by an interactive graphical user interface. The interactivity allows users to effortlessly refine their tourr results by manual intervention, which allows for integration of expert knowledge and aids the interpretation of results. For more information on tourr see Wickham et. al (2011) <doi:10.18637/jss.v040.i02> or <https://github.com/ggobi/tourr>.

This package provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types.

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/>.

The aim of the package is to create data objects which allow for accesses like x["test"] and x["test","test"].

This package implements Latent Unknown Clusters By Integrating Multi-omics Data (LUCID; Peng (2019) <doi:10.1093/bioinformatics/btz667>) for integrative clustering with exposures, multi-omics data, and health outcomes. Supports three integration strategies: early, parallel, and serial. Provides model fitting and tuning, lasso-type regularization for exposure and omics feature selection, handling of missing data, including both sporadic and complete-case patterns, prediction, and g-computation for estimating causal effects of exposures, bootstrap inference for uncertainty estimation, and S3 summary and plot methods. For the multi-omics integration framework, see Jia (2024) <https://journal.r-project.org/articles/RJ-2024-012/RJ-2024-012.pdf>. For the missing-data imputation mechanism, see Jia (2024) <doi:10.1093/bioadv/vbae123>.

This package contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.

Fits sparse generalized linear models using an adaptive ridge approximation to an L0 penalty. Supported model families include Gaussian, logistic, Poisson, gamma, and inverse Gaussian regression. The package also provides cross-validation for selecting the penalty parameter.

Originally design to characterise Aqueous Two Phase Systems, LLSR provide a simple way to analyse experimental data and obtain phase diagram parameters, among other properties, systematically. The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research.

Estimate covariance matrices that contain low rank and sparse components.

This package provides a minimal-dependency client for Large Language Model chat APIs. Supports OpenAI <https://github.com/openai>, Anthropic Claude <https://claude.com/>, Moonshot Kimi <https://www.moonshot.ai/>, Ollama <https://ollama.com/>, and other OpenAI'-compatible endpoints. Includes an agent loop with tool use and a Model Context Protocol client <https://modelcontextprotocol.io/>. API design is derived from the ellmer package, reimplemented with only base R, curl', and jsonlite'.

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

This package implements Lagrangian multiplier smoothing splines for flexible nonparametric regression and function estimation. Provides tools for fitting, prediction, and inference using a constrained optimization approach to enforce smoothness. Supports generalized linear models, Weibull accelerated failure time (AFT) models, quadratic programming constraints, and customizable working-correlation structures, with options for parallel fitting. The core spline construction builds on Ezhov et al. (2018) <doi:10.1515/jag-2017-0029>. Quadratic-programming and SQP details follow Goldfarb & Idnani (1983) <doi:10.1007/BF02591962> and Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>. For smoothing spline and penalized spline background, see Wahba (1990) <doi:10.1137/1.9781611970128> and Wood (2017) <doi:10.1201/9781315370279>. For variance-component and correlation-parameter estimation, see Searle et al. (2006) <ISBN:978-0470009598>. The default multivariate partitioning step uses k-means clustering as in MacQueen (1967).

Fits up to 20 nonlinear lactation curve models to dairy animal milk yield data. Models fitted include exponential, polynomial, mixed logarithmic, inverse polynomial, and sigmoid families published between 1923 and 2000. Supports batch processing of multiple animals from a single CSV file, with flexible selection of animals and models. Produces per-animal parameter tables, goodness-of-fit metrics including R-squared (R2), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and a serial autocorrelation statistic, 15 diagnostic figures, and combined cross-animal comparison outputs. References: <doi:10.1085/jgp.5.4.441>, <doi:10.1038/216164a0>, <doi:10.1016/0301-6226(87)90003-0>, <doi:10.4141/cjas87-067>, <doi:10.3168/jds.S0022-0302(00)75136-8>.

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.

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.

This package provides methods for assessing agreement between repeated measurements obtained by two or more methods using the longitudinal concordance correlation coefficient (LCC). Polynomial mixed-effects models (via nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) <doi:10.1007/s13253-018-0321-1>.

Linear model functions using permutation tests.

Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the LongCART and SurvCART algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.

This package provides a suite of tools for estimating, assessing model fit, simulating from, and visualizing location dependent marked point processes characterized by regularity in the pattern. You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model. ldmppr estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools. Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results. Documentation for the package ldmppr is available in the form of a vignette.

Crabs in the English channel, deer skulls, English monarchs, half-caste Manga characters, Jamaican cities, Shakespeare's The Tempest, drugged up cyclists and sexually transmitted diseases.

Probabilistic record linkage without direct identifiers using only diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ; Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane & Cai (2021) <doi: 10.1093/jamia/ocab187>.

Converts video files to mp3', merges multiple audio files and trims audio files using FFmpeg', which is dynamically downloaded to avoid bundling any third-party binaries. Users must ensure compliance with the license terms of FFmpeg when using the package. See <https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-win64-gpl.zip> for details.