It is devoted to the IVIVC linear level A with numerical deconvolution method. The latter is working for inequal and incompatible timepoints between impulse and response curves. A numerical convolution method is also available. Application domains include pharamaceutical industry QA/QC and R&D together with academic research.

Enhances the R Optimization Infrastructure ('ROI') package with the DEoptim and DEoptimR package. DEoptim is used for unconstrained optimization and DEoptimR for constrained optimization.

R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic.

Routines to interact with the Numerai Machine Learning Tournament API <https://numer.ai>. The functionality includes the ability to automatically download the current tournament data, submit predictions, and to get information for your user.

This package provides interface to the Bioinfo-C (internal name: BIOS') library and utilities. ribiosUtils is a Swiss-knife for computational biology in drug discovery, providing functions and utilities with minimal external dependency and maximal efficiency.

Tool for generating quality reports from cruncher outputs (and calculating series scores). The latest version of the cruncher can be downloaded here: <https://github.com/jdemetra/jwsacruncher/releases>.

Interface to the ChEA3 transcription factor enrichment API. ChEA3 integrates evidence from ChIP-seq, co-expression, and literature resources to prioritize transcription factors regulating a given set of genes. This package provides convenient R functions to query the API, retrieve ranked results across collections (including integrated scores), and standardize output for downstream analysis in R/Bioconductor workflows. See <https://maayanlab.cloud/chea3/> or Keenan (2019) <doi:10.1093/nar/gkz446> for further details.

This package provides a method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. This mainly implements the algorithms described in Wrobel et al. (2019) <doi:10.1111/biom.12963> and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology.

This package provides tools for analyzing stream networks, including graph construction, calculation of Link Magnitude, D-LINK, and export of spatial data. The Link Magnitude (Shreve stream order) method follows Shreve (1966) <doi:10.1086/627137>. The D-LINK metric follows Osborne & Wiley (1992) <doi:10.1139/f92-076>.

This package provides a S4 class has been created such that complex operations can be executed on each cell of a raster map. The raster of objects contains a raster map with the addition of a list of generic objects: one object for each raster cells. It allows to write few lines of R code for complex map algebra. Two environmental applications about frequency analysis of raster map of precipitation and creation of a raster map of soil water retention curves have been presented.

Load data from vk.com api about your communiti users and views, ads performance, post on user wall and etc. For more information see API Documentation <https://vk.com/dev/first_guide>.

Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 <doi:10.1371/journal.pgen.1009923>). Ravages also enables to perform genetic simulations (Bocher et al. 2023 <doi:10.1002/gepi.22529>).

This package contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment.

This package implements popular methods for matching in time-varying observational studies. Matching is difficult in this scenario because participants can be treated at different times which may have an influence on the outcomes. The core methods include: "Balanced Risk Set Matching" from Li, Propert, and Rosenbaum (2011) <doi:10.1198/016214501753208573> and "Propensity Score Matching with Time-Dependent Covariates" from Lu (2005) <doi:10.1111/j.1541-0420.2005.00356.x>. Some functions use the Gurobi optimization back-end to improve the optimization problem speed; the gurobi R package and associated software can be downloaded from <https://www.gurobi.com> after obtaining a license.

This package contains a collection of helper functions to use with rbi', the R interface to LibBi', described in Murray et al. (2015) <doi:10.18637/jss.v067.i10>. It contains functions to adapt the proposal distribution and number of particles in particle Markov-Chain Monte Carlo, as well as calculating the Deviance Information Criterion (DIC) and converting between times in LibBi results and R time/dates.

Provide estimation and data generation tools for the quantile generalized beta regression model. For details, see Bourguignon, Gallardo and Saulo <arXiv:2110.04428> The package also provides tools to perform covariates selection.

R access to the Sequential Monte Carlo Template Classes by Johansen <doi:10.18637/jss.v030.i06> is provided. At present, four additional examples have been added, and the first example from the JSS paper has been extended. Further integration and extensions are planned.

An interface to the BaM (Bayesian Modeling) engine, a Fortran'-based executable aimed at estimating a model with a Bayesian approach and using it for prediction, with a particular focus on uncertainty quantification. Classes are defined for the various building blocks of BaM inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions). The typical usage is as follows: (1) specify the model to be estimated; (2) specify the inference setting (dataset, parameters, error models...); (3) perform Bayesian-MCMC inference; (4) read, analyse and use MCMC samples; (5) perform prediction experiments. Technical details are available (in French) in Renard (2017) <https://hal.science/hal-02606929v1>. Examples of applications include Mansanarez et al. (2019) <doi:10.1029/2018WR023389>, Le Coz et al. (2021) <doi:10.1002/hyp.14169>, Perret et al. (2021) <doi:10.1029/2020WR027745>, Darienzo et al. (2021) <doi:10.1029/2020WR028607> and Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.

Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. Estimation is made using the design of Bottazzi (2004) <https://ideas.repec.org/p/ssa/lemwps/2004-14.html>, where the likelihood is maximized by several optimization procedures using the GNU Scientific Library (GSL)', translated to C++ code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric Laplace distributions.

Offers bathymetric interpolation using Inverse Distance Weighted and Ordinary Kriging via the gstat and terra packages. Other functions focus on quantifying physical aquatic habitats (e.g., littoral, epliminion, metalimnion, hypolimnion) from interpolated digital elevation models (DEMs). Functions were designed to calculate these metrics across water levels for use in reservoirs but can be applied to any DEM and will provide values for fixed conditions. Parameters like Secchi disk depth or estimated photic zone, thermocline depth, and water level fluctuation depth are included in most functions.

Relational Class Analysis (RCA) is a method for detecting heterogeneity in attitudinal data (as described in Goldberg A., 2011, Am. J. Soc, 116(5)).

Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).

This package performs all steps in the credit scoring process. This package allows the user to follow all the necessary steps for building an effective scorecard. It provides the user functions for coarse binning of variables, Weights of Evidence (WOE) transformation, variable clustering, custom binning, visualization, and scaling of logistic regression coefficients. The results will generate a scorecard that can be used as an effective credit scoring tool to evaluate risk. For complete details on the credit scoring process, see Siddiqi (2005, ISBN:047175451X).

Use rmarkdown partials, also know as child documents in knitr', so you can make components for HTML, PDF, and Word documents. The package provides various helper functions to make certain functions easier. You may want to use this package, if you want to flexibly summarise objects using a combination of figures, tables, text, and HTML widgets. Unlike HTML widgets, the output is Markdown and can hence be turn into other output formats than HTML. Currently does not play well with rmarkdown notebooks, not tested with Quarto.