These experimental expression data (5 leukemic CLL B-lymphocyte of aggressive form from GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the Cascade one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

This is an opinionated wrapper around the python-chess package. It allows users to read and write PGN files as well as create and explore game trees such as the ones seen in chess books.

Parallel coordinate plotting with resolutions for large data sets and missing values.

This package provides a collection of functions to extract citation information from R packages and to deal with files in citation file format (<https://citation-file-format.github.io/>), extending the functionality already provided by the citation() function in the utils package.

This package performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.

Germline and somatic locus data which contain the total read depth and B allele read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster model can deal with the SNVs mutation and the CNAs mutation.

Implementation of conceptual properties norming studies, including estimates of CPNs parameters with their corresponding variances and estimates for the sampling process, and a sampling property function based on a modified empirical distribution from the original data.

This package provides functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments. A description of the methods is given in Galagate (2016) <https://drum.lib.umd.edu/handle/1903/18170>.

This package provides a system for creating R Markdown reports with a sequential syntax.

This package implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.

Wrapper around the Canadian Mortgage and Housing Corporation (CMHC) web interface. It enables programmatic and reproducible access to a wide variety of housing data from CMHC.

Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.

CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.

Compare C-statistics (concordance statistics) between two survival models, using either bootstrap resampling (Harrell's C) or Uno's C with perturbation-resampling (from the survC1 package). Returns confidence intervals and a p-value for the difference in C-statistics. Useful for evaluating and comparing predictive performance of survival models. Methods implemented for Uno's C are described in Uno et al. (2011) <doi:10.1002/sim.4154>.

Decorate functions to make them return enhanced output. The enhanced output consists in an object of type chronicle containing the result of the function applied to its arguments, as well as a log detailing when the function was run, what were its inputs, what were the errors (if the function failed to run) and other useful information. Tools to handle decorated functions are included, such as a forward pipe operator that makes chaining decorated functions possible.

This package provides a helpful R6 class and methods for interacting with the Posit Connect Server API along with some meaningful utility functions for regular tasks. API documentation varies by Posit Connect installation and version, but the latest documentation is also hosted publicly at <https://docs.posit.co/connect/api/>.

Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>. For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics <DOI:10.1007/s10614-021-10218-2>.

Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.

This package provides a Shiny application to conduct comprehensive analysis of latent means including the examination of group equivalency, propensity score analysis, measurement invariance analysis, and assessment of latent mean differences of equivalent groups with invariant data. Group equivalency and propensity score analyses are implemented using the MatchIt package [Ho et al. (2011) <doi:10.18637/jss.v042.i08>], ensuring robust control for covariates. Structural equation modeling and invariance testing rely heavily on the lavaan package [Rosseel (2012) <doi:10.18637/jss.v048.i02>], providing a flexible and powerful modeling framework. The application also integrates modified functions from Hammack-Brown et al. (2021) <doi:10.1002/hrdq.21452> to support factor ratio testing and the list-and-delete procedure.

Extension of cmprsk to Stratified and Clustered data. A goodness of fit test for Fine-Gray model is also provided. Methods are detailed in the following articles: Zhou et al. (2011) <doi:10.1111/j.1541-0420.2010.01493.x>, Zhou et al. (2012) <doi:10.1093/biostatistics/kxr032>, Zhou et al. (2013) <doi: 10.1002/sim.5815>.

In meta regression sometimes the studies have multiple effects that are correlated. For this reason cluster robust standard errors must be computed. However, since the clusters are unbalanced the wild bootstrap is suggested. See Oczkowski E. and Doucouliagos H. (2015). "Wine prices and quality ratings: a meta-regression analysis". American Journal of Agricultural Economics, 97(1): 103--121. <doi:10.1093/ajae/aau057> and Cameron A. C., Gelbach J. B. and Miller D. L. (2008). "Bootstrap-based improvements for inference with clustered errors". The Review of Economics and Statistics, 90(3): 414--427. <doi:10.1162/rest.90.3.414>.

This package provides a GUI with which users can construct and interact with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.

Estimate one or two cutpoints of a metric or ordinal-scaled variable in the multivariable context of survival data or time-to-event data. Visualise the cutpoint estimation process using contour plots, index plots, and spline plots. It is also possible to estimate cutpoints based on the assumption of a U-shaped or inverted U-shaped relationship between the predictor and the hazard ratio. Govindarajulu, U., and Tarpey, T. (2022) <doi:10.1080/02664763.2020.1846690>.

Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.