Comprehensive set of tools for performing system identification of both linear and nonlinear dynamical systems directly from data. The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis.

R wrapper around the argon HTML library. More at <https://demos.creative-tim.com/argon-design-system/>.

Calculate ActiGraph counts from the X, Y, and Z axes of a triaxial accelerometer. This work was inspired by Neishabouri et al. who published the article "Quantification of Acceleration as Activity Counts in ActiGraph Wearables" on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) and python implementation of this code (<https://github.com/actigraph/agcounts>).

The Brazilian Jurimetrics Association (ABJ in Portuguese, see <https://abj.org.br/> for more information) is a non-profit organization which aims to investigate and promote the use of statistics and probability in the study of Law and its institutions. This package has a set of datasets commonly used in our book.

This package provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. This package enables users to fit non-linear and linear adsorption isotherm modelsâ Freundlich, Langmuir, and Temkinâ within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) <doi:10.1201/b14835>, and Gamerman & Lopes (2006) <doi:10.1201/9781482296426>.

The AIPW package implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the AIPW package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. <doi:10.1093/aje/kwab207>". Visit: <https://yqzhong7.github.io/AIPW/> for more information.

Assists the evaluation of whether and where to focus code optimization, using Amdahl's law and visual aids based on line profiling. Amdahl's profiler organizes profiling output files (including memory profiling) in a visually appealing way. It is meant to help to balance development vs. execution time by helping to identify the most promising sections of code to optimize and projecting potential gains. The package is an addition to R's standard profiling tools and is not a wrapper for them.

Using of the accelerated line search algorithm for simultaneously diagonalize a set of symmetric positive definite matrices.

Plot stacked areas and confidence bands as filled polygons, or add polygons to existing plots. A variety of input formats are supported, including vectors, matrices, data frames, formulas, etc.

This package provides a tool that improves the prediction performance of multilevel regression with post-stratification (MrP) by combining a number of machine learning methods. For information on the method, please refer to Broniecki, Wüest, Leemann (2020) Improving Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP) in the Journal of Politics'. Final pre-print version: <https://lucasleemann.files.wordpress.com/2020/07/automrp-r2pa.pdf>.

Download air quality and meteorological information of Chile from the National Air Quality System (S.I.N.C.A.)<https://sinca.mma.gob.cl/> dependent on the Ministry of the Environment and the Meteorological Directorate of Chile (D.M.C.)<https://www.meteochile.gob.cl/> dependent on the Directorate General of Civil Aeronautics.

This package provides functions to analyse overdispersed counts or proportions. These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod.

This package provides alternatives to the normal adjusted R-squared estimator for the estimation of the multiple squared correlation in regression models, as fitted by the lm() function. The alternative estimators are described in Karch (2020) <DOI:10.1525/collabra.343>.

The generated wealth of immune repertoire sequencing data requires software to investigate and quantify inter- and intra-antibody repertoire evolution to uncover how B cells evolve during immune responses. Here, we present AntibodyForests', a software to investigate and quantify inter- and intra-antibody repertoire evolution.

This package implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>.

It provides the density, distribution function, quantile function, random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for the three parameter Asymmetric Laplace Distribution defined in Koenker and Machado (1999). This is a special case of the skewed family of distributions available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression.

This package performs Bayesian prediction of complex computer codes when fast approximations are available. It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O'Hagan (2000), Biometrika 87(1):1.

Designed for optimal use in performing fast, accurate walking strides segmentation from high-density data collected from a wearable accelerometer worn during continuous walking activity.

R codes for the (adaptive) Sum of Powered Score ('SPU and aSPU') tests, inverse variance weighted Sum of Powered score ('SPUw and aSPUw') tests and gene-based and some pathway based association tests (Pathway based Sum of Powered Score tests ('SPUpath'), adaptive SPUpath ('aSPUpath') test, GEEaSPU test for multiple traits - single SNP (single nucleotide polymorphism) association in generalized estimation equations, MTaSPUs test for multiple traits - single SNP association with Genome Wide Association Studies ('GWAS') summary statistics, Gene-based Association Test that uses an extended Simes procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version of GATES test for pathway-based association testing ('GATES-Simes'). ). The tests can be used with genetic and other data sets with covariates. The response variable is binary or quantitative. Summary; (1) Single trait-'SNP set association with individual-level data ('aSPU', aSPUw', aSPUr'), (2) Single trait-'SNP set association with summary statistics ('aSPUs'), (3) Single trait-pathway association with individual-level data ('aSPUpath'), (4) Single trait-pathway association with summary statistics ('aSPUsPath'), (5) Multiple traits-single SNP association with individual-level data ('GEEaSPU'), (6) Multiple traits- single SNP association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath').

Interface package for sala', the spatial network analysis library from the depthmapX software application. The R parts of the code are based on the rdepthmap package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) <https://discovery.ucl.ac.uk/id/eprint/2651> "Depthmap 4: a researcher's handbook".

Estimate aquatic species life history using robust techniques. This package supports users undertaking two types of analysis: 1) Growth from length-at-age data, and 2) maturity analyses for length and/or age data. Maturity analyses are performed using generalised linear model approaches incorporating either a binomial or quasibinomial distribution. Growth modelling is performed using the multimodel approach presented by Smart et al. (2016) "Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future" <doi:10.1111/faf.12154>.

This package provides tools to study sorting patterns in matching markets and to estimate the affinity matrix of both the bipartite one-to-one matching model without frictions and with Transferable Utility by Dupuy and Galichon (2014) <doi:10.1086/677191> and its unipartite variant by Ciscato', Galichon and Gousse (2020) <doi:10.1086/704611>. It also contains all the necessary tools to implement the saliency analysis, to run rank tests of the affinity matrix and to build tables and plots summarizing the findings.

We propose an age-dependent topic modelling (ATM) model, providing a low-rank representation of longitudinal records of hundreds of distinct diseases in large electronic health record data sets. The model assigns to each individual topic weights for several disease topics; each disease topic reflects a set of diseases that tend to co-occur as a function of age, quantified by age-dependent topic loadings for each disease. The model assumes that for each disease diagnosis, a topic is sampled based on the individualâ s topic weights (which sum to 1 across topics, for a given individual), and a disease is sampled based on the individualâ s age and the age-dependent topic loadings (which sum to 1 across diseases, for a given topic at a given age). The model generalises the Latent Dirichlet Allocation (LDA) model by allowing topic loadings for each topic to vary with age. References: Jiang (2023) <doi:10.1038/s41588-023-01522-8>.

The functions in this package inspect, read, edit and run files for APSIM "Next Generation" ('JSON') and APSIM "Classic" ('XML'). The files with an apsim extension correspond to APSIM Classic (7.x) - Windows only - and the ones with an apsimx extension correspond to APSIM "Next Generation". For more information about APSIM see (<https://www.apsim.info/>) and for APSIM next generation (<https://apsimnextgeneration.netlify.app/>).