Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The CancerGram model is too large for CRAN and it has to be downloaded separately from the repository: <https://github.com/BioGenies/CancerGramModel>. For more information see: Burdukiewicz et al. (2020) <doi:10.3390/pharmaceutics12111045>.

Sample and cell filtering as well as visualisation of output metrics from Cell Ranger by Grace X.Y. Zheng et al. (2017) <doi:10.1038/ncomms14049>. CRMetrics allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. CRMetrics allows for easy removal of ambient RNA using SoupX by Matthew D Young and Sam Behjati (2020) <doi:10.1093/gigascience/giaa151> or CellBender by Stephen J Fleming et al. (2022) <doi:10.1101/791699>. Furthermore, it is possible to preprocess data using Pagoda2 by Nikolas Barkas et al. (2021) <https://github.com/kharchenkolab/pagoda2> or Seurat by Yuhan Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048> followed by embedding of cells using Conos by Nikolas Barkas et al. (2019) <doi:10.1038/s41592-019-0466-z>. Finally, doublets can be detected using scrublet by Samuel L. Wolock et al. (2019) <doi:10.1016/j.cels.2018.11.005> or DoubletDetection by Gayoso et al. (2020) <doi:10.5281/zenodo.2678041>. In the end, cells are filtered based on user input for use in downstream applications.

This package provides functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978â 0â 387â 72578â 9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.

This package provides functions designed to simulate data that conform to basic unidimensional IRT models (for now 3-parameter binary response models and graded response models) along with Post-Hoc CAT simulations of those models given various item selection methods, ability estimation methods, and termination criteria. See Wainer (2000) <doi:10.4324/9781410605931>, van der Linden & Pashley (2010) <doi:10.1007/978-0-387-85461-8_1>, and Eggen (1999) <doi:10.1177/01466219922031365> for more details.

Estimation and inference for linear models where some or all of the fixed-effects coefficients are subject to order restrictions. This package uses the robust residual bootstrap methodology for inference, and can handle some structure in the residual variance matrix.

Various utilities for the complex multivariate Gaussian distribution and complex Gaussian processes.

This package provides functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.

Offers a set of objects tailored to simplify working with choice data. It enables the computation of choice probabilities and the likelihood of various types of choice models based on given data.

Method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.

This package provides the datasets from Efron & Hastie (2016, ISBN: 9781108107952), "Computer Age Statistical Inference: Algorithms, Evidence, and Data Science", in an accessible R format for those who want to use them for study or to try to reproduce analyses from the book.

Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.

This package provides a collection of clinical trial example datasets from multiple sources including the CDISC Pilot 01 study (CDISC <https://www.cdisc.org/>). All datasets are provided in Parquet format for efficient storage and can be accessed using the connector package. Designed for training, testing, prototyping, and demonstrating clinical data analysis workflows.

Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references.

This package provides tools for interacting with the Circle CI API (<https://circleci.com/docs/api/v2/>). Besides executing common tasks such as querying build logs and restarting builds, this package also helps setting up permissions to deploy from builds.

This package implements higher order likelihood-based inference for logistic and loglinear models.

Calculate the likelihood ratio test p-value and likelihood confidence intervals for misspecified Cox models, as described in Shao and Guo (2025) <doi:10.48550/arXiv.2508.11851>.

Get description of images from Clarifai API. For more information, see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come up with descriptive labels of the things in an image. It also provides how confident it is about each of the labels.

This package implements a kernel-based association test for copy number variation (CNV) aggregate analysis in a certain genomic region (e.g., gene set, chromosome, or genome) that is robust to the within-locus and across-locus etiological heterogeneity, and bypass the need to define a "locus" unit for CNVs. Brucker, A., et al. (2020) <doi:10.1101/666875>.

Analyzing responses to check-all-that-apply survey items often requires data transformations and subjective decisions for combining categories. CATAcode contains tools for exploring response patterns, facilitating data transformations, applying a set of decision rules for coding responses, and summarizing response frequencies.

This high-level API client provides open access to cryptocurrency market data, sentiment indicators, and interactive charting tools. The data is sourced from major cryptocurrency exchanges via curl and returned in xts'-format. The data comes in open, high, low, and close (OHLC) format with flexible granularity, ranging from seconds to months. This flexibility makes it ideal for developing and backtesting trading strategies or conducting detailed market analysis.

Computes effect sizes, standard errors, and confidence intervals for total, direct, and indirect effects in continuous-time mediation models as described in Pesigan, Russell, and Chow (2025) <doi:10.1037/met0000779>.

This package provides a collection of functions for top-down exploratory data analysis of spectral data including nuclear magnetic resonance (NMR), infrared (IR), Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy. Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate for this type of high-dimensional data are available. ChemoSpec is designed for structured experiments, such as metabolomics investigations, where the samples fall into treatment and control groups. Graphical output is formatted consistently for publication quality plots. ChemoSpec is intended to be very user friendly and to help you get usable results quickly. A vignette covering typical operations is available.

Perceptually uniform palettes for commonly used variables in oceanography as functions taking an integer and producing character vectors of colours. See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M. and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for the guidelines adhered to when creating the palettes.

Comparison of two ROC curves through the methodology proposed by Ana C. Braga.