This package provides a two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.

Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.

Collaborative writing and editing of R Markdown (or Sweave) documents. The local .Rmd (or .Rnw) is uploaded as a plain-text file to Google Drive. By taking advantage of the easily readable Markdown (or LaTeX) syntax and the well-known online interface offered by Google Docs, collaborators can easily contribute to the writing and editing process. After integrating all authorsâ contributions, the final document can be downloaded and rendered locally.

Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.

Travel Time API <https://docs.traveltime.com/api/overview/introduction> helps users find locations by journey time rather than using â as the crow fliesâ distance. Time-based searching gives users more opportunities for personalisation and delivers a more relevant search.

This package provides data frames for forest or tree data structures. You can create forest data structures from data frames and process them based on their hierarchies.

Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.

Providing new german-wide TapeR Models and functions for their evaluation. Included are the most common tree species in Germany (Norway spruce, Scots pine, European larch, Douglas fir, Silver fir as well as European beech, Common/Sessile oak and Red oak). Many other species are mapped to them so that 36 tree species / groups can be processed. Single trees are defined by species code, one or multiple diameters in arbitrary measuring height and tree height. The functions then provide information on diameters along the stem, bark thickness, height of diameters, volume of the total or parts of the trunk and total and component above-ground biomass. It is also possible to calculate assortments from the taper curves. Uncertainty information is provided for diameter, volume and component biomass estimation.

This package provides data sets for teaching statistics and data science courses. It includes a sample of data from John Edmund Kerrich's famous coinflip experiment. These are data that I used for statistics. The package also contains an R Markdown template with the required formatting for assignments in my former courses.

Instance feature calculation and evolutionary instance generation for the traveling salesman problem. Also contains code to "morph" two TSP instances into each other. And the possibility to conveniently run a couple of solvers on TSP instances.

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>.

This package provides functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to download NCBI data dumps and create a local database for fast and local taxonomic assignment.

This package provides a calculator for the two-dimensional clinical Disease Activity index for Psoriatic Arthritis (TwoDcDAPSA), a principal component-derived measure that complements the conventional clinical DAPSA score. The TwoDcDAPSA captures residual variation in patient-reported outcomes (pain and patient global assessment) and joint counts (swollen and tender) after adjusting for standardized cDAPSA using natural spline coefficients derived from published models. Residuals are standardized and combined with fixed principal component loadings to yield a continuous PROs-Joint Contrast (PJC) score and quartile groupings. The package applies pre-specified coefficients and loadings to new datasets but does not estimate spline models or principal components itself.

This package provides functions for imputing missing item responses for dichotomous and polytomous test and assessment data. This package enables missing imputation methods that are suitable for test and assessment data, including: listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), item mean imputation (IM), two-way (TW) and response function (RF) imputation, (see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>), logistic regression (LR) imputation, predictive mean matching (PMM), and expectationâ maximization (EM) imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).

Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.

This package provides a graphics output device for R that records plots in a LaTeX-friendly format. The device transforms plotting commands issued by R functions into LaTeX code blocks. When included in a LaTeX document, these blocks are interpreted with the help of TikZ'---a graphics package for TeX and friends written by Till Tantau. Using the tikzDevice', the text of R plots can contain LaTeX commands such as mathematical formula. The device also allows arbitrary LaTeX code to be inserted into the output stream.

Fits 2D and 3D geometric transformations via Stan probabilistic programming engine ( Stan Development Team (2021) <https://mc-stan.org>). Returns posterior distribution for individual parameters of the fitted distribution. Allows for computation of LOO and WAIC information criteria (Vehtari A, Gelman A, Gabry J (2017) <doi:10.1007/s11222-016-9696-4>) as well as Bayesian R-squared (Gelman A, Goodrich B, Gabry J, and Vehtari A (2018) <doi:10.1080/00031305.2018.1549100>).

This package provides methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) <doi:10.48550/arXiv.2108.04731>.

Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.

This package performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Glog (Durbin et al., 2002) <doi:10.1093/bioinformatics/18.suppl_1.S105>, Neglog (Whittaker et al., 2005) <doi:10.1111/j.1467-9876.2005.00520.x>, Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) <doi:10.1002/sta4.104>, Bickel-Docksum (Bickel and Doksum, 1981) <doi:10.1080/01621459.1981.10477649>, Yeo-Johnson (Yeo and Johnson, 2000) <doi:10.1093/biomet/87.4.954>, Square Root (Medina et al., 2019), Manly (Manly, 1976) <doi:10.2307/2988129>, Modulus (John and Draper, 1980) <doi:10.2307/2986305>, Dual (Yang, 2006) <doi:10.1016/j.econlet.2006.01.011>, Gpower (Kelmansky et al., 2013) <doi:10.1515/sagmb-2012-0030>. It also performs graphical approaches, assesses the success of the transformation via tests and plots.

This package performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components.

This package implements two tests for same-source of toolmarks. The chumbley_non_random() test follows the paper "An Improved Version of a Tool Mark Comparison Algorithm" by Hadler and Morris (2017) <doi:10.1111/1556-4029.13640>. This is an extension of the Chumbley score as previously described in "Validation of Tool Mark Comparisons Obtained Using a Quantitative, Comparative, Statistical Algorithm" by Chumbley et al (2010) <doi:10.1111/j.1556-4029.2010.01424.x>. fixed_width_no_modeling() is based on correlation measures in a diamond shaped area of the toolmark as described in Hadler (2017).

Adds some functions to help in your coding etiquette. tinycodet primarily focuses on 4 aspects. 1) Safer decimal (in)equality testing, standard-evaluated alternatives to with() and aes(), and other functions for safer coding. 2) A new package import system, that attempts to combine the benefits of using a package without attaching it, with the benefits of attaching a package. 3) Extending the string manipulation capabilities of the stringi R package. 4) Reducing repetitive code. Besides linking to Rcpp', tinycodet has only one other dependency, namely stringi'.

Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.