An integrated set of tools for thermodynamic calculations in aqueous geochemistry and geobiochemistry. Functions are provided for writing balanced reactions to form species from user-selected basis species and for calculating the standard molal properties of species and reactions, including the standard Gibbs energy and equilibrium constant. Calculations of the non-equilibrium chemical affinity and equilibrium chemical activity of species can be portrayed on diagrams as a function of temperature, pressure, or activity of basis species; in two dimensions, this gives a maximum affinity or predominance diagram. The diagrams have formatted chemical formulas and axis labels, and water stability limits can be added to Eh-pH, oxygen fugacity- temperature, and other diagrams with a redox variable. The package has been developed to handle common calculations in aqueous geochemistry, such as solubility due to complexation of metal ions, mineral buffers of redox or pH, and changing the basis species across a diagram ("mosaic diagrams"). CHNOSZ also implements a group additivity algorithm for the standard thermodynamic properties of proteins.

This package provides similar functionality to Microsoft Excel CUMPRINC function <https://support.microsoft.com/en-us/office/cumprinc-function-94a4516d-bd65-41a1-bc16-053a6af4c04d>. Returns principal remaining at a given month, principal paid in a month, and accumulated principal paid at a given month based on original loan amount, monthly interest rate, and term of loan.

This package provides a reliable and efficient tool for cleaning univariate time series data. It implements reliable and efficient procedures for automating the process of cleaning univariate time series data. The package provides integration with already developed and deployed tools for missing value imputation and outlier detection. It also provides a way of visualizing large time-series data in different resolutions.

Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.

When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) <doi:10.1080/10618600.2022.2071905>.

Generate random numbers from the Cryptographically Secure Pseudorandom Number Generator (CSPRNG) provided by the underlying operating system. System CSPRNGs are seeded internally by the OS with entropy it gathers from the system hardware. The following system functions are used: arc4random_buf() on macOS and BSD; BCryptgenRandom() on Windows; Sys_getrandom() on Linux.

This package provides a simple way to write ".Rprofile" code in an R Markdown file and have it knit to the correct location for your operating system.

Java JAR files for the Apache Commons Mathematics Library for use by users and other packages.

The cyclotomic numbers are complex numbers that can be thought of as the rational numbers extended with the roots of unity. They are represented exactly, enabling exact computations. They contain the Gaussian rationals (complex numbers with rational real and imaginary parts) as well as the square roots of all rational numbers. They also contain the sine and cosine of all rational multiples of pi. The algorithms implemented in this package are taken from the Haskell package cyclotomic', whose algorithms are adapted from code by Martin Schoenert and Thomas Breuer in the GAP project (<https://www.gap-system.org/>). Cyclotomic numbers have applications in number theory, algebraic geometry, algebraic number theory, coding theory, and in the theory of graphs and combinatorics. They have connections to the theory of modular functions and modular curves.

Create CUSUM (cumulative sum) statistics from a vector or dataframe. Also create single or faceted CUSUM control charts, with or without control limits. Accepts vector, dataframe, tibble or data.table inputs.

The primary motivation of this package is to take the things that are great about the R packages flextable <https://davidgohel.github.io/flextable/> and officer <https://davidgohel.github.io/officer/>, take the standard and complex pieces of formatting clinical tables for regulatory use, and simplify the tedious pieces.

This package provides functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.

This package contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.

Flexible univariate count models based on renewal processes. The models may include covariates and can be specified with familiar formula syntax as in glm() and package flexsurv'. The methodology is described by Kharrat et all (2019) <doi:10.18637/jss.v090.i13> (included as vignette Countr_guide in the package).

Calculates daily climate water balance for irrigation purposes and also calculates the reference evapotranspiration (ET) using three methods, Penman and Monteith (Allen et al. 1998, ISBN:92-5-104219-5); Priestley and Taylor (1972) <doi:10/cr3qwn>; or Hargreaves and Samani (1985) <doi:10.13031/2013.26773>. Users may specify a management allowed depletion (MAD), which is used to suggest when to irrigate. The functionality allows for the use of crop and water stress coefficients as well.

Corbae-Ouliaris frequency domain filtering. According to Corbae and Ouliaris (2006) <doi:10.1017/CBO9781139164863.008>, this is a solution for extracting cycles from time series, like business cycles etc. when filtering. This method is valid for both stationary and non-stationary time series.

Client for the Open Citations Corpus (<http://opencitations.net/>). Includes a set of functions for getting one identifier type from another, as well as getting references and citations for a given identifier.

This package provides a large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.

This package implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data.

Computes the coverage correlation coefficient introduced in <doi:10.48550/arXiv.2508.06402> , a statistical measure that quantifies dependence between two random vectors by computing the union volume of data-centered hypercubes in a uniform space.

This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.

Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) <doi:10.1175/WAF-D-13-00027.1>.

Maximum likelihood estimation of the Cauchy-Cacoullos (discrete Cauchy) distribution. Probability mass, distribution and quantile function are also included. The reference paper is: Papadatos N. (2022). "The Characteristic Function of the Discrete Cauchy Distribution in Memory of T. Cacoullos". Journal of Statistical Theory Practice, 16(3): 47. <doi:10.1007/s42519-022-00268-6>.

This package provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.