Multiple Imputation for Proteomics

A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) . It is dedicated to dealing with multiple imputation for proteomics.

Make 'Mirai' 'Promises'

Allows 'mirai' objects encapsulating asynchronous computations, from the 'mirai' package by Gao (2023) , to be used interchangeably with 'promise' objects from the 'promises' package by Cheng (2021) < https://CRAN.R-project.org/package=promises>. This facilitates their use with packages 'plumber' by Schloerke and Allen (2022) < https://CRAN.R-project.org/package=plumber> and 'shiny' by Cheng, Allaire, Sievert, Schloerke, Xie, Allen, McPherson, Dipert and Borges (2022) < https://CRAN.R-project.org/package=shiny>.

Analysis and Identification of Raman Spectra of Microplastics

Pre-processing and polymer identification of Raman spectra of plastics. Pre-processing includes normalisation functions, peak identification based on local maxima, smoothing process and removal of spectral region of no interest. Polymer identification can be performed using Pearson correlation coefficient or Euclidean distance (Renner et al. (2019), ), and the comparison can be done with a user-defined database or with the database already implemented in the package, which currently includes 356 spectra, with several spectra of plastic colorants.

Procrustes Matching for Latent Space Item Response Model

Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) , and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) .

Load Estimation of River Compounds with Different Methods

Implements several of the most popular load estimation procedures, including averaging methods, ratio estimators and regression methods. The package provides an easy-to-use tool to rapidly calculate the load for various compounds and to compare different methods. The package also supplies additional functions to easily organize and analyze the data.

Methods for Fixed-Income Valuation, Risk and Return

Bond Pricing and Fixed-Income Valuation of Selected Securities included here serve as a quick reference of Quantitative Methods for undergraduate courses on Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and Return. CFA Institute ("CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 107-151, pp. 237-299)", 2019, ISBN: 9781119593577). Barbara S. Petitt ("Fixed Income Analysis", 2019, ISBN: 9781119628132). Frank J. Fabozzi ("Handbook of Finance: Financial Markets and Instruments", 2008, ISBN: 9780470078143). Frank J. Fabozzi ("Fixed Income Analysis", 2007, ISBN: 9780470052211).

In Vitro Toxicokinetic Data Processing and Analysis Pipeline

A set of tools for processing and analyzing in vitro toxicokinetic measurements in a standardized and reproducible pipeline. The package was developed to perform frequentist and Bayesian estimation on a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The methods provided by the package were described in Wambaugh et al. (2019) .

In Vitro Toxicokinetic Data Processed with the 'invitroTKstats' Pipeline

A collection of datasets containing a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The datasets provided by the package were processed and analyzed with the companion 'invitroTKstats' package.

Bayesian BIN (Bias, Information, Noise) Model of Forecasting

A recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" .

A Unified Time Series Event Detection Framework

By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) .