This package performs AnchorRegression proposed by Rothenhäusler et al. 2020. The code is adapted from the original paper repository. (<https://github.com/rothenhaeusler/anchor-regression>) The code was developed independently from the authors of the paper.

Set of functions for analyzing Atomic Force Microscope (AFM) force-distance curves. It allows to obtain the contact and unbinding points, perform the baseline correction, estimate the Young's modulus, fit up to two exponential decay function to a stress-relaxation / creep experiment, obtain adhesion energies. These operations can be done either over a single F-d curve or over a set of F-d curves in batch mode.

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.

Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform ANOVA with post-hoc tests (e.g. Tukey HSD and Duncan MRR), compute coefficients of variation, and generate publication-ready summaries. High-level wrappers allow automated multi-variable analysis with optional clustering by experimental factors, as well as direct export of results to Excel spreadsheets and high-resolution image tables for reporting. Functions build on ggplot2', stats', and related packages and follow methods widely used in agronomy (field trials and plant breeding). Key references include Tukey (1949) <doi:10.2307/3001913>, Duncan (1955) <doi:10.2307/3001478>, and Cohen (1988, ISBN:9781138892899); see also agricolae <https://CRAN.R-project.org/package=agricolae> and Wickham (2016, ISBN:9783319242750> for ggplot2'. Versión en español: Conjunto de herramientas para el análisis estadà stico, visualización y generación de reportes en ensayos agroindustriales y agrà colas. Incluye funciones para ANOVA con pruebas post-hoc, resúmenes automáticos multivariables con o sin agrupamiento por factores, y exportación directa de resultados a Excel e imágenes de alta resolución para informes técnicos.

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>.

This package provides a collection of tools for the estimation of animals home range.

This package provides functions to perform statistical inference in the balanced one-way ANOVA model with a random factor: confidence intervals, prediction interval, and Weerahandi generalized pivotal quantities. References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) <doi:10.1007/978-1-4612-0825-9>; Lin & Liao (2008) <doi:10.1016/j.jspi.2008.01.001>.

This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. The Generalized Discrimination Score is a generic forecast verification framework which can be applied to any of the following verification contexts: dichotomous, polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble. A comprehensive description of the Generalized Discrimination Score, including all equations used in this package, is provided by Mason and Weigel (2009) <doi:10.1175/MWR-D-10-05069.1>.

Fast processing of ArcGIS FeatureCollection protocol buffers in R. It is designed to work seamlessly with httr2 and integrates with sf'.

Inference of protein complex states from quantitative proteomics data. The package takes information on known stable protein interactions (i.e. protein components of the same complex) and assesses how protein quantitative ratios change between different conditions. It reports protein pairs for which relative protein quantities to each other have been significantly altered in the tested condition.

This package provides a modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See <doi:10.4018/IJQSPR.2016010102> for an overview of applicability domains.

Providing ways to estimate the value of European stock options given historical stock price data. It includes functions for calculating option values based on autoregressiveâ moving-average (ARMA) models and generates information about these models. This package is made to be easy to understand and for financial analysis capabilities.

This package creates pre- and post- intervention scattergrams based on audiometric data. These scattergrams are formatted for publication in Otology & Neurotology and other otolaryngology journals. For more details, see Gurgel et al (2012) <doi:10.1177/0194599812458401>, Oghalai and Jackler (2016) <doi:10.1177/0194599816638314>.

Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.

Anytime-valid inference for linear models, namely, sequential t-tests, sequential F-tests, and confidence sequences with time-uniform Type-I error and coverage guarantees. This allows hypotheses to be continuously tested without sacrificing false positive guarantees. It is based on the methods documented in Lindon et al. (2022) <doi:10.48550/arXiv.2210.08589>.

Trigger animation effects on scroll on any HTML element of shiny and rmarkdown', such as any text or plot, thanks to the AOS Animate On Scroll jQuery library.

Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) <doi:10.1016/S0925-2312(01)00702-0>.

Interface to the Azure Machine Learning Software Development Kit ('SDK'). Data scientists can use the SDK to train, deploy, automate, and manage machine learning models on the Azure Machine Learning service. To learn more about Azure Machine Learning visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.

This package provides a lightweight, dependency-free toolbox for pre-processing XY data from experimental methods (i.e. any signal that can be measured along a continuous variable). This package provides methods for baseline estimation and correction, smoothing, normalization, integration and peaks detection. Baseline correction methods includes polynomial fitting as described in Lieber and Mahadevan-Jansen (2003) <doi:10.1366/000370203322554518>, Rolling Ball algorithm after Kneen and Annegarn (1996) <doi:10.1016/0168-583X(95)00908-6>, SNIP algorithm after Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, 4S Peak Filling after Liland (2015) <doi:10.1016/j.mex.2015.02.009> and more.

Supplies a set of functions to query air travel data for user- specified years and airports. Datasets include on-time flights, airlines, airports, planes, and weather.

This package provides a collection of functions for computing centrographic statistics (e.g., standard distance, standard deviation ellipse, standard deviation box) for observations taken at point locations. Separate plotting functions have been developed for each measure. Users interested in writing results to ESRI shapefiles can do so by using results from aspace functions as inputs to the convert.to.shapefile() and write.shapefile() functions in the shapefiles library. We intend to provide terra integration for geographic data in a future release. The aspace package was originally conceived to aid in the analysis of spatial patterns of travel behaviour (see Buliung and Remmel 2008 <doi:10.1007/s10109-008-0063-7>).

Probability surveys often use auxiliary continuous data from administrative records, but the utility of this data is diminished when it is discretized for confidentiality. We provide a set of survey estimators to make full use of information from the discretized variables. See Williams, S.Z., Zou, J., Liu, Y., Si, Y., Galea, S. and Chen, Q. (2024), Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data. Statistics in Medicine, 43: 5803-5813. <doi:10.1002/sim.10270> for details.

Randomly splits data into testing and training sets. Then, uses stepwise selection to fit numerous multiple regression models on the training data, and tests them on the test data. Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5).

The successor to the AlphaSim software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the Markovian Coalescent Simulator ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].