An implementation of the additive heredity model for the mixture-of-mixtures experiments of Shen et al. (2019) in Technometrics <doi:10.1080/00401706.2019.1630010>. The additive heredity model considers an additive structure to inherently connect the major components with the minor components. The additive heredity model has a meaningful interpretation for the estimated model because of the hierarchical and heredity principles applied and the nonnegative garrote technique used for variable selection.

Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments. The APCtools package offers visualization techniques and general routines to simplify the workflow of an APC analysis. Sophisticated functions are available both for descriptive and regression model-based analyses. For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization techniques building on the concept of Lexis diagrams. Model-based analyses build on the separation of the temporal dimensions based on generalized additive models, where a tensor product interaction surface (usually between age and period) is utilized to represent the third dimension (usually cohort) on its diagonal. Such tensor product surfaces can also be estimated while accounting for further covariates in the regression model. See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.

La libreria ACEP contiene funciones especificas para desarrollar analisis computacional de eventos de protesta. Asimismo, contiene base de datos con colecciones de notas sobre protestas y diccionarios de palabras conflictivas. Coleccion de diccionarios que reune diccionarios de diferentes origenes. The ACEP library contains specific functions to perform computational analysis of protest events. It also contains a database with collections of notes on protests and dictionaries of conflicting words. Collection of dictionaries that brings together dictionaries from different sources.

The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.

This package provides a dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published MATLAB code by Hay et al. (2019) <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review).

This package provides several methods for aggregating probabilistic forecasts. You have a group of people who have made probabilistic forecasts for the same event. You want to take advantage of the "wisdom of the crowd" and combine these forecasts in some sensible way. This package provides implementations of several strategies, including geometric mean of odds, an extremized aggregate (Neyman, Roughgarden (2021) <doi:10.1145/3490486.3538243>), and "high-density trimmed mean" (Powell et al. (2022) <doi:10.1037/dec0000191>).

This package provides functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589> for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.

Estimate the lower and upper bound of asymptomatic cases in an epidemic using the capture/recapture methods from Böhning et al. (2020) <doi:10.1016/j.ijid.2020.06.009> and Rocchetti et al. (2020) <doi:10.1101/2020.07.14.20153445>. Note there is currently some discussion about the validity of the methods implemented in this package. You should read carefully the original articles, alongside this answer from Li et al. (2022) <doi:10.48550/arXiv.2209.11334> before using this package in your project.

The empirical cumulative average deviation function introduced by the author is utilized to develop both Ad- and Ud-plots. The Ad-plot can identify symmetry, skewness, and outliers of the data distribution, including anomalies. The Ud-plot created by slightly modifying Ad-plot is exceptional in assessing normality, outperforming normal QQ-plot, normal PP-plot, and their derivations. The d-value that quantifies the degree of proximity between the Ud-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Full description of this methodology can be found in the article by Wijesuriya (2025) <doi:10.1080/03610926.2024.2440583>.

Allows access to selected services that are part of the Google Adwords API <https://developers.google.com/adwords/api/docs/guides/start>. Google Adwords is an online advertising service by Google', that delivers Ads to users. This package offers a authentication process using OAUTH2'. Currently, there are two methods of data of accessing the API, depending on the type of request. One method uses SOAP requests which require building an XML structure and then sent to the API. These are used for the ManagedCustomerService and the TargetingIdeaService'. The second method is by building AWQL queries for the reporting side of the Google Adwords API.

Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.

This package provides function declarations and inline function definitions that facilitate communication between R and the Armadillo C++ library for linear algebra and scientific computing. This implementation is derived from Vargas Sepulveda and Schneider Malamud (2024) <doi:10.1016/j.softx.2025.102087>.

This package provides functions to produce accessible HTML slides, HTML', Word and PDF documents from input R markdown files. Accessible PDF files are produced only on a Windows Operating System. One aspect of accessibility is providing a headings structure that is recognised by a screen reader, providing a navigational tool for a blind or partially-sighted person. A key aim is to produce documents of different formats easily from each of a collection of R markdown source files. Input R markdown files are rendered using the render() function from the rmarkdown package <https://cran.r-project.org/package=rmarkdown>. A zip file containing multiple output files can be produced from one function call. A user-supplied template Word document can be used to determine the formatting of an output Word document. Accessible PDF files are produced from Word documents using OfficeToPDF <https://github.com/cognidox/OfficeToPDF>. A convenience function, install_otp() is provided to install this software. The option to print HTML output to (non-accessible) PDF files is also available.

This package provides functions to fit the binomial and multinomial additive hazard models and to estimate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) and extended by Yokota et al (2017).

Stanford ATLAS (Advanced Temporal Search Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. This package is designed to interface directly with an instance of ATLAS search engine and facilitates API queries and data dumps. Prerequisite is a good knowledge of the temporal language to be able to efficiently construct a query. More information available at <https://shahlab.stanford.edu/start>.

This package provides functions for age-period-cohort analysis. Aggregate data can be organised in matrices indexed by age-cohort, age-period or cohort-period. The data can include dose and response or just doses. The statistical model is a generalized linear model (GLM) allowing for 3,2,1 or 0 of the age-period-cohort factors. 2-sample analysis is possible. Mixed frequency data are possible. Individual-level data should have a row for each individual and columns for each of age, period, and cohort. The statistical model for repeated cross-section is a generalized linear model. The statistical model for panel data is ordinary least squares. The canonical parametrisation of Kuang, Nielsen and Nielsen (2008) <DOI:10.1093/biomet/asn026> is used. Thus, the analysis does not rely on ad hoc identification.

This package provides functions for implementing the Analysis-of-marginal-Tail-Means (ATM) method, a robust optimization method for discrete black-box optimization. Technical details can be found in Mak and Wu (2018+) <arXiv:1712.03589>. This work was supported by USARO grant W911NF-17-1-0007.

This package provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) <https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1>.

Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. Designed for AD Model Builder ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as Template Model Builder models and other special cases. Functionality is similar to Stan', and the rstan and shinystan packages are used for diagnostics and inference.

Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.

Client package for the AWS Key Management Service <https://aws.amazon.com/kms/>, a cloud service for managing encryption keys.

An interactive document on the topic of one-way and two-way analysis of variance using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://tinyurl.com/ANOVAStatsTool>.

Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, Oâ Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1016/j.ajhg.2023.07.001>.

Functionality to add, delete, read and update table records from your AppSheet apps, using the official API <https://api.appsheet.com/>.