This package provides R users with direct access to genomic and clinical data from the cBioPortal web resource via user-friendly functions that wrap cBioPortal's existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations, copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'), microsatellite instability status ('MSI'), FACETS and select clinical data points (depending on the study). See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.

Various statistical methods for survival analysis in comparing survival curves between two groups, including overall hypothesis tests described in Li et al. (2015) <doi:10.1371/journal.pone.0116774> and Huang et al. (2020) <doi:10.1080/03610918.2020.1753075>, fixed-point tests in Klein et al. (2007) <doi:10.1002/sim.2864>, short-term tests, and long-term tests in Logan et al. (2008) <doi:10.1111/j.1541-0420.2007.00975.x>. Some commonly used descriptive statistics and plots are also included.

Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A description of CIU can be found in e.g. Främling (2020) <doi:10.1007/978-3-030-51924-7_4>.

Estimation of Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) <doi:10.1111/1467-9965.00114> as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) <doi:10.1111/j.1467-9868.2005.00508.x> are included.

This package contains the CONCOR (CONvergence of iterated CORrelations) algorithm and a series of supplemental functions for easy running, plotting, and blockmodeling. The CONCOR algorithm is used on social network data to identify network positions based off a definition of structural equivalence; see Breiger, Boorman, and Arabie (1975) <doi:10.1016/0022-2496(75)90028-0> and Wasserman and Faust's book Social Network Analysis: Methods and Applications (1994). This version allows multiple relationships for the same set of nodes and uses both incoming and outgoing ties to find positions.

We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S., Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See <doi:10.1080/00031305.2020.1816213> for a detailed presentation of the method.

Allows users to identify similar cases for qualitative case studies using statistical matching methods.

This package performs cryptographic randomness tests on a sequence of random integers or bits. Included tests are greatest common divisor, birthday spacings, book stack, adaptive chi-square, topological binary, and three random walk tests (Ryabko and Monarev, 2005) <doi:10.1016/j.jspi.2004.02.010>. Tests except greatest common divisor and birthday spacings are not covered by standard test suites. In addition to the chi-square goodness-of-fit test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are also generated by some of the cryptographic randomness tests.

Computes conditional multivariate normal densities, probabilities, and random deviates.

This package creates project specific directory and file templates that are written to a .Rprofile file. Upon starting a new R session, these templates can be used to streamline the creation of new directories that are standardized to the user's preferences and can include the initiation of a git repository, an RStudio R project, and project-local dependency management with the renv package.

This package provides functions for reading in and manipulating CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 data.

An API wrapper for Cryptowatch to get prices and other information (e.g., volume, trades, order books, bid and ask prices, live quotes, and more) about cryptocurrencies and crypto exchanges. See <https://docs.cryptowat.ch/rest-api> for a detailed documentation.

Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) <doi:10.48550/arXiv.1707.01815>, Gaure (2013) <doi:10.1016/j.csda.2013.03.024>, Berge (2018) <https://ideas.repec.org/p/luc/wpaper/18-13.html>, and Correia et al. (2020) <doi: 10.1177/1536867X20909691>.

Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.

Light weight implementation of the standard distribution functions for the chi distribution, wrapping those for the chi-squared distribution in the stats package.

Hardware-based support for CRC32C cyclic redundancy checksum function is made available for x86_64 systems with SSE2 support as well as for arm64', and detected at build-time via cmake with a software-based fallback. This functionality is exported at the C'-language level for use by other packages. CRC32C is described in RFC 3270 at <https://datatracker.ietf.org/doc/html/rfc3720> and is based on Castagnoli et al <doi:10.1109/26.231911>.

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) <doi:10.1534/genetics.117.300257>.

Find multiple solutions of a nonlinear least squares problem. Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gaussâ Newton method. Optimization and Engineering, 1-31. Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) <doi:10.1002/psp4.13055>. Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67. GPT based helper bot available at <https://chatgpt.com/g/g-684936db9e748191a2796debb00cd755-cluster-gauss-newton-method-helper-bot> .

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

Allow to run Cppcheck (<https://cppcheck.sourceforge.io/>) on C and C++ files with a R command or a RStudio addin. The report appears in the RStudio viewer pane as a formatted HTML file. It is also possible to get this report with a shiny application. Cppcheck can spot many error types and it can also give some recommendations on the code.

Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. csa package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.

Defines the classes used for "class comparison" problems in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class comparison includes tests for differential expression; see Simon's book for details on typical problem types.

This package provides a novel visualization technique for plotting timestamped events on a 24-hour circular clock face. This is particularly useful for analyzing daily patterns, event clustering, and gaps in temporal data. The package also generalizes this approach to create cyclic charts for other periods, including weekly and monthly cycles, enabling effective event planning and pattern analysis across multiple time frames.

This package implements convex regression with interpretable sharp partitions (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.