Fits look-up tables by filling entries with the mean or median values of observations fall in partitions of the feature space. Partitions can be determined by user of the package using input argument feature.boundaries, and dimensions of the feature space can be any combination of continuous and categorical features provided by the data set. A Predict function directly fetches corresponding entry value, and a default value is defined as the mean or median of all available observations. The table and other components are represented using the S4 class lookupTable.

Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) <DOI:10.1002/gepi.20042>.

Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering. See Einbeck, Tutz and Evers (2005) <doi:10.1007/s11222-005-4073-8> and Ameijeiras-Alonso and Einbeck (2023) <doi:10.1007/s11634-023-00575-1>.

Estimation of life expectancy and Life Years Lost (LYL, or lillies for short) for a given population, for example those with a given disease or condition. In addition, the package can be used to compare estimates from different populations, or to estimate confidence intervals. Technical details of the method are available in Plana-Ripoll et al. (2020) <doi:10.1371/journal.pone.0228073> and Andersen (2017) <doi:10.1002/sim.7357>.

This package provides tools are provided to expand vectors of short URLs into long URLs'. No API services are used, which may mean that this operates more slowly than API services do (since they usually cache results of expansions that every user of the service requests). You can setup your own caching layer with the memoise package if you wish to have a speedup during single sessions or add larger dependencies, such as Redis', to gain a longer-term performance boost at the expense of added complexity.

This package provides a set of tools designed to enhance transparency and understanding of date-time manipulation functions from the lubridate package. It provides detailed feedback about the operations performed by lubridate functions, allowing users to better comprehend and debug their code. These insights serve as both a learning tool for newcomers and a debugging aid for programmers working with date-time data.

This package provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values.

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.

This package provides functions to estimate and visualize linear as well as nonlinear impulse responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>. The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.

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".

Helpers for customizing selected outputs from lavaan by Rosseel (2012) <doi:10.18637/jss.v048.i02> and print them. The functions are intended to be used by package developers in their packages and so are not designed to be user-friendly. They are designed to be let developers customize the tables by other functions. Currently the parameter estimates tables of a fitted object are supported.

Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.

Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.

Obtain least-squares means for linear, generalized linear, and mixed models. Compute contrasts or linear functions of least-squares means, and comparisons of slopes. Plots and compact letter displays. Least-squares means were proposed in Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers", Tech Report ARS-20-8, USDA National Agricultural Library, and discussed further in Searle, Speed, and Milliken (1980) "Population marginal means in the linear model: An alternative to least squares means", The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>. NOTE: lsmeans now relies primarily on code in the emmeans package. lsmeans will be archived in the near future.

Changes of landscape diversity and structure can be detected soon if relying on landscape class combinations and analysing patterns at multiple scales. LandComp provides such an opportunity, based on Juhász-Nagy's functions (Juhász-Nagy P, Podani J 1983 <doi:10.1007/BF00129432>). Functions can handle multilayered data. Requirements of the input: binary data contained by a regular square or hexagonal grid, and the grid should have projected coordinates.

Calculates Land Surface Temperature from Landsat band 10 and 11. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). <doi: 10.1109/TGRS.2008.2007125>. Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). <doi:10.1016/j.rse.2004.02.003>. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). <doi: 10.1016/j.asr.2009.01.023>. Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). <doi:10.1016/0034-4257(96)00039-9>. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). <doi:10.1080/01431169308904400>. Land Surface Temperature Retrieval from Landsat 8 TIRSâ Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). <doi:10.3390/rs6109829>. Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. SkokoviÄ D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014).

An interface to LuaJIT <https://luajit.org>, a just-in-time compiler for the Lua scripting language <https://www.lua.org>. Allows users to run Lua code from R'.

Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step. It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques. The package explains non expert users how hierarchical clustering algorithms work.

Runtime for serving containers that can execute R code on the AWS Lambda serverless compute service <https://aws.amazon.com/lambda/>. Provides the necessary functionality for handling the various endpoints required for accepting new input and sending responses.

Imports a data frame containing a single time resolved laser ablation mass spectrometry analysis of a foraminifera (or other carbonate shell), then detects when the laser has burnt through the foraminifera test as a function of change in signal over time.

We provide a solution for performing permutation tests on linear and mixed linear regression models. It allows users to obtain accurate p-values without making distributional assumptions about the data. By generating a null distribution of the test statistics through repeated permutations of the response variable, permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023) <doi:10.1007/s10683-023-09799-6>). In this early version, we focus on the permutation tests over observed t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null distribution of the test statistic through repeated permutations of the response variable, each observed t values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop criterion (Anscombe (1953) <doi:10.1111/j.2517-6161.1953.tb00121.x>) is adopted to force permutation to stop if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so, we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate p-values.

This package provides a framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The akmedoids package is available from <https://github.com/MAnalytics/akmedoids>.

Import, processing, validation, and visualization of personal light exposure measurement data from wearable devices. The package implements features such as the import of data and metadata files, conversion of common file formats, validation of light logging data, verification of crucial metadata, calculation of common parameters, and semi-automated analysis and visualization.