This contains functions that can be used to estimate the time-dependent precision-recall curve (PRC) and the corresponding area under the PRC for right-censored survival data. It also compute time-dependent ROC curve and its corresponding area under the ROC curve (AUC). See Beyene, Chen and Kifle (2024) <doi:10.1002/bimj.202300135>.

Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), <doi:10.18637/jss.v046.i05>).

Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) <doi:10.1016/j.ijforecast.2015.01.006>.

This package provides a simple type annotation for R that is usable in scripts, in the R console and in packages. It is intended as a convention to allow other packages to use the type information to provide error checking, automatic documentation or optimizations.

This package provides a screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects. This package also provides two screening functions for screening high-dimensional predictors when the events are rare. The firth method is called Rare-Screening which employs a repeated random sampling with replacement and using linear modeling with Bayes adjustment. The Second method is called Firth-ttScreening which uses ttScreening method with additional Firth correction term in the maximum likelihood for the logistic regression model. These methods handle the high-dimensionality and low event rates.

Find the optimal decision rules (AKA progression criteria) and sample size for clinical trials with three (stop/pause/go) outcomes. Both binary and continuous endpoints can be accommodated, as can cases where an adjustment is planned following a pause outcome. For more details see Wilson et al. (2024) <doi:10.1186/s12874-024-02351-x>.

Sensitivity analysis using the trimmed means estimator.

Schedule R scripts/processes with the Windows task scheduler. This allows R users to automate R processes on specific time points from R itself.

You only need to type why pie charts are bad on Google to find thousands of articles full of (valid) reasons why other types of charts should be preferred over this one. Therefore, because of the little use due to the reasons already mentioned, making pie charts (and related) in R is not straightforward, so other functions are needed to simplify things. In this R package there are useful functions to make tasty pie charts immediately by exploiting the many cool templates provided.

The Common Workflow Language <https://www.commonwl.org/> is an open standard for describing data analysis workflows. This package takes the raw Common Workflow Language workflows encoded in JSON or YAML and turns the workflow elements into tidy data frames or lists. A graph representation for the workflow can be constructed and visualized with the parsed workflow inputs, outputs, and steps. Users can embed the visualizations in their Shiny applications, and export them as HTML files or static images.

This is a companion package for the text2sdg package. It contains the trained ensemble models needed by the detect_sdg function from the text2sdg package. See Wulff, Meier and Mata (2023) <arXiv:2301.11353> and Meier, Wulff and Mata (2021) <arXiv:2110.05856> for reference.

Use the <https://toggl.com> time tracker api through R.

This package provides a toolkit of tidy data manipulation verbs with data.table as the backend. Combining the merits of syntax elegance from dplyr and computing performance from data.table', tidyfst intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of data.table'. While enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations.

The tmap package provides two plotting modes for static and interactive thematic maps. This package extends tmap with two additional modes based on Mapbox GL JS and MapLibre GL JS'. These modes feature interactive vector tiles, globe views, and other modern web-mapping capabilities, while maintaining a consistent tmap interface across all plotting modes.

Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.

This package provides functions are provided for prior specification in divergence time estimation using fossils as well as other kinds of data. It provides tools for interacting with the input and output of Bayesian platforms in evolutionary biology such as BEAST2', MrBayes', RevBayes', or MCMCTree'. It Implements a simple measure similarity between probability density functions for comparing prior and posterior Bayesian densities, as well as code for calculating the combination of distributions using conflation of Hill (2008). Functions for estimating the origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and stratigraphic intervals (Marshall 2010) are also available. Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. <doi:10.48550/arXiv.0808.1808>, Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. <DOI:10.1002/9781118625590>, Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. <DOI:10.1017/S1089332600001911>.

Create "good enough" tables with a single formula. tablespan tables can be exported to Excel', HTML', LaTeX', and RTF by leveraging the packages openxlsx and gt'. See <https://jhorzek.github.io/tablespan/> for an introduction.

This package provides threshold sweep methods for Qualitative Comparative Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an exploratory tool for discovering causal structures. Built on top of the QCA package by Dusa (2019) <doi:10.1007/978-3-319-75668-4>, with function arguments following QCA conventions. Based on set-theoretic methods by Ragin (2008) <doi:10.7208/chicago/9780226702797.001.0001> and established robustness protocols by Rubinson et al. (2019) <doi:10.1177/00491241211036158>.

This package provides tools for evaluating the trustworthiness of machine learning models in production and research settings. Computes a Stability Index that quantifies the consistency of model predictions across multiple runs or resamples, and a Robustness Score that measures model resilience under small input perturbations. Designed for data scientists, ML engineers, and researchers who need to monitor and ensure model reliability, reproducibility, and deployment readiness.

Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. Tracer (<https://github.com/beast-dev/tracer/>) is a GUI tool to parse and analyze the files generated by BEAST2'. This package provides a way to parse and analyze BEAST2 input files without active user input, but using R function calls instead.

Implement text and sentiment analysis with texter'. Generate sentiment scores on text data and also visualize sentiments. texter allows you to quickly generate insights on your data. It includes support for lexicons such as NRC and Bing'.

Computation of t-year survival probabilities and t-year risks with right censored survival data. The Kaplan-Meier estimator is used to provide estimates for data without competing risks and the Aalen-Johansen estimator is used when there are competing risks. Confidence intervals and p-values are obtained using either usual Wald-type inference or empirical likelihood inference, as described in Thomas and Grunkemeier (1975) <doi:10.1080/01621459.1975.10480315> and Blanche (2020) <doi:10.1007/s10985-018-09458-6>. Functions for both one-sample and two-sample inference are provided. Unlike Wald-type inference, empirical likelihood inference always leads to consistent conclusions, in terms of statistical significance, when comparing two risks (or survival probabilities) via either a ratio or a difference.

Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.