Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response data. Predicting mixture toxicity based on reference models such as concentration addition', independent action', and generalized concentration addition'.

This package provides a collection of functions for computations and visualizations of microbial pan-genomes.

Create variable width bar charts i.e. "bar mekko" charts to include important quantitative context. Closely related to mosaic, spine (or spinogram), matrix, submarine, olympic, Mondrian or product plots and tree maps.

This package implements generalized maximum entropy estimation for linear regression, kink regression, and smooth transition kink regression models. The approach represents unknown parameters and disturbances as probability distributions over discrete support spaces and estimates them by maximizing entropy subject to model constraints. It is particularly suited to ill-posed problems and does not require distributional assumptions on the error term. The methods have been applied in empirical studies such as Tarkhamtham and Yamaka (2019) <https://thaijmath.com/index.php/thaijmath/article/view/867/870> and Maneejuk, Yamaka, and Sriboonchitta (2022) <doi:10.1080/03610918.2020.1836214>.

Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references are Royston and Altman (1994) <doi:10.2307/2986270> and Royston and Sauerbrei (2008, ISBN:978-0-470-02842-1). In addition, it can model a sigmoid relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) <doi:10.1177/1536867X1401400206>. This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling.

This package provides a graphical user interface for the MuToss Project.

Implementation of two tools to merge Hardware Event Monitors (HEMs) from different subexperiments. Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, <doi:10.1109/TCAD.2020.3013051>. For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC 21). Association for Computing Machinery. <doi:10.1145/3412841.3441931>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

This package provides a multi action button for usage in shiny applications.

This package provides a sample size calculator for micro-randomized trials (MRTs) with binary outcomes based on Cohn et al. (2023) <doi:10.1002/sim.9748>. Also provides a power calculator when the sample size is input by the user.

R Client for the Microsoft Cognitive Services Web Language Model REST API, including Break Into Words, Calculate Conditional Probability, Calculate Joint Probability, Generate Next Words, and List Available Models. A valid account MUST be registered at the Microsoft Cognitive Services website <https://www.microsoft.com/cognitive-services/> in order to obtain a (free) API key. Without an API key, this package will not work properly.

This package provides modules as an organizational unit for source code. Modules enforce to be more rigorous when defining dependencies and have a local search path. They can be used as a sub unit within packages or in scripts.

There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, meta2d is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, meta3d is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual.

Simulates the Multi-Attribute Search and Choice (MASC) model of Gluth, Deakin and Rieskamp (2026) <doi:10.1037/rev0000614> for multi-attribute decision-making, including sequential information search, Bayesian belief updating, and choice. Beliefs may be treated as univariate (independent attributes), or multivariate over correlated attributes ('MASC-C'), in which observing one attribute updates beliefs about correlated attributes via a Kalman filter.

The Cauchy distribution is a special case of the t distribution when the degrees of freedom are equal to 1. The functions are related to the multivariate Cauchy distribution and include simulation, computation of the density, maximum likelihood estimation, contour plot of the bivariate Cauchy distribution, and discriminant analysis. References include: Nadarajah S. and Kotz S. (2008). "Estimation methods for the multivariate t distribution". Acta Applicandae Mathematicae, 102(1): 99--118. <doi:10.1007/s10440-008-9212-8>, and Kanti V. Mardia, John T. Kent and John M. Bibby (1979). "Multivariate analysis", ISBN:978-0124712522. Academic Press, London.

This package performs the multiple testing procedures of Cox (2011) <doi:10.5170/CERN-2011-006> and Wong and Cox (2007) <doi:10.1080/02664760701240014>.

Modern model-based geostatistics for point-referenced data. This package provides a simple interface to run spatial machine learning models and geostatistical models that estimate a continuous (raster) surface from point-referenced outcomes and, optionally, a set of raster covariates. The package also includes functions to summarize raster outcomes by (polygon) region while preserving uncertainty.

This package implements seven estimators for average treatment effect (ATE) estimation in mixed-subjects designs (MSDs), where human subjects data is augmented with predictions from large language models (LLMs). Includes Difference-in-Means, GREG, PPI++, Doubly-Tuned, Difference-in-Predictions (DiP), DiP++, and D-T DiP estimators. Provides point estimates, variance estimation via delta-method or bootstrap, and optimal design selection for budget allocation between human observations and LLM predictions.

Calculates mean cumulative count (MCC) to estimate the expected cumulative number of recurrent events per person over time in the presence of competing risks and censoring. Implements both the Dong-Yasui equation method and sum of cumulative incidence method described in Dong, et al. (2015) <doi:10.1093/aje/kwu289>. Supports inverse probability weighting for causal inference as outlined in Gaber, et al. (2023) <doi:10.1093/aje/kwad031>. Provides S3 methods for printing, summarizing, plotting, and extracting results. Handles grouped analyses and integrates with ggplot2 <https://ggplot2.tidyverse.org/> for visualization.

Modeling microstructures of human tooth dentin and horizontal serial-sectioning of the dentin. Corresponding age range of dentin serial sections, that is used in stable isotope analyses, can be calculated by using this package.

Multidimensional unfolding using Schoenemann's algorithm for metric and Procrustes rotation of unfolding results.

This package provides tools for univariate and multivariate generalized linear models with model averaging and null model technique.

This package provides methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.

This package provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) <doi:10.1081/STA-120025379>, including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) <doi:10.48550/arXiv.2209.04757> for smoothing multivariate data on half-spaces.

This package implements modern resampling and permutation methods for robust statistical inference without restrictive parametric assumptions. Provides bias-corrected and accelerated (BCa) bootstrap (Efron and Tibshirani (1993) <doi:10.1201/9780429246593>), wild bootstrap for heteroscedastic regression (Liu (1988) <doi:10.1214/aos/1176351062>, Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>), block bootstrap for time series (Politis and Romano (1994) <doi:10.1080/01621459.1994.10476870>), and permutation-based multiple testing correction (Westfall and Young (1993) <ISBN:0-471-55761-7>). Methods handle non-normal data, heteroscedasticity, time series correlation, and multiple comparisons.