It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.

Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.

Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template R Markdown reports contained in the package.

Typical morphological profiling datasets have millions of cells and hundreds of features per cell. When working with this data, you must clean the data, normalize the features to make them comparable across experiments, transform the features, select features based on their quality, and aggregate the single-cell data, if needed. cytominer makes these steps fast and easy. Methods used in practice in the field are discussed in Caicedo (2017) <doi:10.1038/nmeth.4397>. An overview of the field is presented in Caicedo (2016) <doi:10.1016/j.copbio.2016.04.003>.

This package provides functionality for computing support intervals for univariate parameters based on confidence intervals or parameter estimates with standard errors (Pawel et al., 2022) <doi:10.48550/arXiv.2206.12290>.

Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.

This package provides Python'-style list comprehensions. List comprehension expressions use usual loops (for(), while() and repeat()) and usual if() as list producers. In many cases it gives more concise notation than standard "*apply + filter" strategy.

This package provides a collection of functions to generate a large variety of structures in high dimensions. These data structures are useful for testing, validating, and improving algorithms used in dimensionality reduction, clustering, machine learning, and visualization.

Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.

Synthesizing joint distributions from marginal densities, focusing on controlling key statistical properties such as correlation for continuous data, mutual information for categorical data, and inducing Simpson's Paradox. Generate datasets with specified correlation structures for continuous variables, adjust mutual information between categorical variables, and manipulate subgroup correlations to intentionally create Simpson's Paradox. Joe (1997) <doi:10.1201/b13150> Sklar (1959) <https://en.wikipedia.org/wiki/Sklar%27s_theorem>.

The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.

Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.

This package provides a system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) <doi:10.1101/2023.03.11.532179>.

In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various statistics are computed. This package can also be used for analyzing the results from random experiments.

Plots the coefficients from model objects. This very quickly shows the user the point estimates and confidence intervals for fitted models.

This package provides a collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. ChemoSpec2D deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the sample from a statistical perspective. In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. ChemoSpec2D takes many of its cues from ChemoSpec and tries to create consistent graphical output and to be very user friendly.

This package implements a changepoint-aware ensemble forecasting algorithm that combines Theta, TBATS (Trigonometric, Box-Cox transformation, ARMA errors, Trend, Seasonal components), and ARFIMA (AutoRegressive, Fractionally Integrated, Moving Average) using a product-of-experts approach for robust probabilistic prediction.

Facilitates dynamic exploration of text collections through an intuitive graphical user interface and the power of regular expressions. The package contains 1) a helper function to convert a data frame to a corporaexplorerobject and 2) a Shiny app for fast and flexible exploration of a corporaexplorerobject'. The package also includes demo apps with which one can explore Jane Austen's novels and the State of the Union Addresses (data from the janeaustenr and sotu packages respectively).

This package provides functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments. A description of the methods is given in Galagate (2016) <https://drum.lib.umd.edu/handle/1903/18170>.

Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method.

This package provides a simple runner for fuzz-testing functions in an R package's public interface. Fuzz testing helps identify functions lacking sufficient argument validation, and uncovers problematic inputs that, while valid by function signature, may cause issues within the function body.

This package provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2020) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures. Moreover, as of version 4.0, it is the only method of its kind that provides measures for model evaluation and selection that are custom-made for the problem of INUS-discovery.

This package creates a common framework for organizing, naming, and gathering population, age, race, and ethnicity data from the Census Bureau. Accesses the API <https://www.census.gov/data/developers/data-sets.html>. Provides tools for adding information to existing data to line up with Census data.

Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published.