This package provides a problem solving environment (PSE) for fitting separable nonlinear models to measurements arising in physics and chemistry experiments, as described by Mullen & van Stokkum (2007) <doi:10.18637/jss.v018.i03> for its use in fitting time resolved spectroscopy data, and as described by Laptenok et al. (2007) <doi:10.18637/jss.v018.i08> for its use in fitting Fluorescence Lifetime Imaging Microscopy (FLIM) data, in the study of Förster Resonance Energy Transfer (FRET). `TIMP` also serves as the computation backend for the `GloTarAn` software, a graphical user interface for the package, as described in Snellenburg et al. (2012) <doi:10.18637/jss.v049.i03>.

Differential analysis of tumor tissue immune cell type abundance based on RNA-seq gene-level expression from The Cancer Genome Atlas (TCGA; <https://pancanatlas.xenahubs.net>) database.

Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell (2017) <doi:10.1021/acs.est.7b01613>.

This package creates a local Lightning Memory-Mapped Database ('LMDB') of many commonly used taxonomic authorities and provides functions that can quickly query this data. Supported taxonomic authorities include the Integrated Taxonomic Information System ('ITIS'), National Center for Biotechnology Information ('NCBI'), Global Biodiversity Information Facility ('GBIF'), Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). Name and identifier resolution using LMDB can be hundreds of times faster than either relational databases or internet-based queries. Precise data provenance information for data derived from naming providers is also included.

This package provides comprehensive tools for conducting research using the Thinking Grid framework, a psychological measurement approach for understanding the stream of thought. Includes functions for generating Qualtrics surveys with the thinking grid, processing survey responses, calculating quadrant depths, and creating various visualization types including heatmaps, animations, and statistical plots. See Irving, Z. C., Murray, S., Kuvar, V., Urena, M., and Mills, C. (2025) "Consciousness, Just in Time: Fluctuations in the Stream of Consciousness during Tasks and Rest" (manuscript under review, draft available from zci7c@virginia.edu).

Includes functions for mapping named lists to function arguments, random strings, pasting and combining rows together across columns, etc.

Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) <doi:10.1111/2041-210X.14038>.

This package implements combined p-value functions for two trials along with compatible combined point and interval estimates as described in Pawel, Roos, and Held (2025) <doi:10.48550/arXiv.2503.10246>.

This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).

For multiple ranked input lists (full or partial) representing the same set of N objects, the package TopKLists <doi:10.1515/sagmb-2014-0093> offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results. Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN. See <https://github.com/pievos101/TopKLists> for installation instructions.

The twelvedata REST service offers access to current and historical data on stocks, standard as well as digital crypto currencies, and other financial assets covering a wide variety of course and time spans. See <https://twelvedata.com/> for details, to create an account, and to request an API key for free-but-capped access to the data.

The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

This package provides methods and tools for generating forecasts at different temporal frequencies using a hierarchical time series approach.

Tautulli (<http://tautulli.com>) is a monitoring application for Plex Media Servers (<https://www.plex.tv>) which collects a lot of data about media items and server usage such as play counts. This package interacts with the Tautulli API of any specified server to get said data into R. The Tautulli API documentation is available at <https://github.com/Tautulli/Tautulli/blob/master/API.md>.

Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time duration are handled. Both can be regularly or unevenly spaced (overlapping duration are allowed). Only POSIX* format are used for dates and times. The following classes are provided : POSIXcti', POSIXctp', TimeIntervalDataFrame', TimeInstantDataFrame', SubtimeDataFrame ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).

Allow to compute and visualise convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized C++ code linked into R via Rcpp'. Highly efficient engine allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational Numerical Weather Prediction models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe thunderstorms. The most relevant methods used in the package based on the following publications Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>, McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>, Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>, Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>, Showalter (1953) <doi:10.1175/1520-0477-34.6.250>, Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>, Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>, Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>, Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>, Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>, Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.

This package provides a kernel of functions for programming time series methods in a way that is relatively independently of the representation of time. Also provides plotting, time windowing, and some other utility functions which are specifically intended for time series. See the Guide distributed as a vignette, or ?tframe.Intro for more details. (User utilities are in package tfplot.).

An R re-implementation of the treeinterpreter package on PyPI <https://pypi.org/project/treeinterpreter/>. Each prediction can be decomposed as prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <doi:10.48550/arXiv.1906.10845>.

Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." <doi:10.1016/S0266-8920(01)00019-4>, Breitung (1989) "Asymptotic approximations for probability integrals." <doi:10.1016/0266-8920(89)90024-6>.

This package provides a comprehensive suite of statistical tools for analyzing, simulating, and computing properties of the Topp-Leone Cauchy Rayleigh (TLCAR) distribution, a versatile distribution amalgamating features of the Topp-Leone, Cauchy, and Rayleigh distributions, ideal for modeling intricate, heterogeneous data across scientific domains. See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) <doi:10.1007/s44199-023-00066-4> and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) <doi:10.1007/s44199-023-00069-1> for further insights.

STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.

This package provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the DoseFinding package, which is used for the fitting of the models. See <doi:10.1080/01621459.2017.1281813> for details.

Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.