This package provides different datasets parsed from Drugbank <https://www.drugbank.ca/covid-19> database using dbparser package. It is a smaller version from dbdataset package. It contains only information about COVID-19 possible treatment.

This package contains the Correlates of State Policy Project dataset (+ codebook) assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy> used by the cspp package. The Correlates data contains over 3000 variables across more than 100 years that pertain to state politics and policy in the United States.

Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with corx'.

This package provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.

Fit continuous-time correlated random walk models with time indexed covariates to animal telemetry data. The model is fit using the Kalman-filter on a state space version of the continuous-time stochastic movement process.

This package contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.

Data recorded as paths or trajectories may be suitably described by curves, which are independent of their parametrization. For the space of such curves, the package provides functionalities for reading curves, sampling points on curves, calculating distance between curves and for computing Tukey curve depth of a curve w.r.t. to a bundle of curves. For details see Lafaye De Micheaux, Mozharovskyi, and Vimond (2021) <doi:10.48550/arXiv.1901.00180>.

This package provides a minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis.

Conditional moments test, as proposed by Newey (1985) <doi:10.2307/1911011 > and Tauchen (1985) <doi:10.1016/0304-4076(85)90149-6>, useful to detect specification violations for models estimated by maximum likelihood. Methods for probit and tobit models are provided.

Analyzes and modifies metabolomics raw data (generated using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or an Isotopic-Ratio approach. Quality control plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.

Get programmatic access to the open data provided by the Czech Statistical Office (CZSO, <https://csu.gov.cz>).

This package implements controlled interrupted time series (CITS) analysis for evaluating interventions in comparative time-series data. The package provides tools for preparing panel time-series datasets, fitting models using generalized least squares (GLS) with optional autoregressiveâ moving-average (ARMA) error structures, and computing fitted values and robust standard errors using cluster-robust variance estimators (CR2). Visualization functions enable clear presentation of estimated effects and counterfactual trajectories following interventions. Background on methods for causal inference in interrupted time series can be found in Linden and Adams (2011) <doi:10.1111/j.1365-2753.2010.01504.x> and Lopez Bernal, Cummins, and Gasparrini (2018) <doi:10.1093/ije/dyy135>.

This package provides estimates of several summary measures for clinical trials including the average hazard ratio, the weighted average hazard ratio, the restricted superiority probability ratio, the restricted mean survival difference and the ratio of restricted mean times lost, based on the short-term and long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) which accommodates various non-proportional hazards scenarios. The inference procedures and the asymptotic results for the summary measures are discussed in Yang (2018, <doi:10.1002/sim.7676>).

Generates skeletons of closed 2D polygons using Voronoi diagrams. It provides methods for sf', terra', and geos objects to compute polygon centerlines based on the generated skeletons. Voronoi, G. (1908) <doi:10.1515/crll.1908.134.198>.

Tests convergence in macro-financial panels combining Dynamic Factor Models (DFM) and mean-reverting Ornstein-Uhlenbeck (OU) processes. Provides: (i) static/approximate DFMs for large panels with VAR/VECM stability checks, Portmanteau tests and rolling out-of-sample R^2, following Stock and Watson (2002) <doi:10.1198/073500102317351921> and the Generalized Dynamic Factor Model of Forni, Hallin, Lippi and Reichlin (2000) <doi:10.1162/003465300559037>; (ii) cointegration analysis à la Johansen (1988) <doi:10.1016/0165-1889(88)90041-3>; (iii) OU-based convergence and half-life summaries grounded in Uhlenbeck and Ornstein (1930) <doi:10.1103/PhysRev.36.823> and Vasicek (1977) <doi:10.1016/0304-405X(77)90016-2>; (iv) robust inference via sandwich HC/HAC estimators (Zeileis (2004) <doi:10.18637/jss.v011.i10>) and regression diagnostics ('lmtest'); and (v) optional PLS-based factor preselection (Mevik and Wehrens (2007) <doi:10.18637/jss.v018.i02>). Functions emphasize reproducibility and clear, publication-ready summaries.

In computer experiments space-filling designs are having great impact. Most popularly used space-filling designs are Uniform designs (UDs), Latin hypercube designs (LHDs) etc. For further references one can see Mckay (1979) <DOI:10.1080/00401706.1979.10489755> and Fang (1980) <https://cir.nii.ac.jp/crid/1570291225616774784>. In this package, we have provided algorithms for generate efficient LHDs and UDs. Here, generated LHDs are efficient as they possess lower value of Maxpro measure, Phi_p value and Maximum Absolute Correlation (MAC) value based on the weightage given to each criterion. On the other hand, the produced UDs are having good space-filling property as they always attain the lower bound of Discrete Discrepancy measure. Further, some useful functions added in this package for adding more value to this package.

In metabolic flux experiments tracer molecules (often glucose containing labelled carbon) are incorporated in compounds measured using mass spectrometry. The mass isotopologue distributions of these compounds needs to be corrected for natural abundance of labelled carbon and other effects, which are specific on the compound and ionization technique applied. This package provides functions to correct such effects in gas chromatography atmospheric pressure chemical ionization mass spectrometry analyses.

This package provides functions for the input/output and visualization of medical imaging data in the form of CIFTI files <https://www.nitrc.org/projects/cifti/>.

This package provides methods for analyzing (cell) motion in two or three dimensions. Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>, Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>, Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>, Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>, Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>, Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>, Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.

Classifies the type of cancer using routinely collected data commonly found in cancer registries from pathology reports. The package implements the International Classification of Diseases for Oncology, 3rd Edition site (topography), histology (morphology), and behaviour codes of neoplasms to classify cancer type <https://www.who.int/standards/classifications/other-classifications/international-classification-of-diseases-for-oncology>. Classification in children utilize the International Classification of Childhood Cancer by Steliarova-Foucher et al. (2005) <doi:10.1002/cncr.20910>. Adolescent and young adult cancer classification is based on Barr et al. (2020) <doi:10.1002/cncr.33041>.

An engine for stochastic cellular automata. It provides a high-level interface to declare a model, which can then be simulated by various backends (Genin et al. (2023) <doi:10.1101/2023.11.08.566206>).

Visualizes results of item analysis such as item difficulty, item discrimination, and coefficient alpha for ease of result communication.

This package contains the adaptation of bubblebath from MATLAB', developed by Adam Danz and available through the MATLAB Central File Exchange, and the tools to transform a dataframe of radii and points to plot-able paths.

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