Estimation of Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) <doi:10.1111/1467-9965.00114> as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) <doi:10.1111/j.1467-9868.2005.00508.x> are included.

The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint's checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) Microsoft refreshes the Austria CRAN mirror on the "Microsoft R Archived Network" server (<https://mran.microsoft.com/>). Immediately after completion of the rsync mirror process, the process takes a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17.

Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square statistic. Implemented are the tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998) <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>, Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010) <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996> included for comparison. The utility functions nested.to.contingency and paired.to.contingency convert data between various useful formats. Thyroids and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989) <doi:10.1016/0165-1781(89)90196-0> respectively.

The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission.

This package provides analytical methods for analyzing CRISPR screen data at different levels of gene expression. Multi-component normal mixture models and EM algorithms are used for modeling.

Data personally collected about the spread of COVID-19 (SARS-COV-2) in Tunisia <https://github.com/MounaBelaid/covid19datatunisia>.

Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.

This package implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.

Create descriptive tables for continuous and categorical variables. Apply summary statistics and counting function, with or without a grouping variable, and create beautiful reports using rmarkdown or officer'. You can also compute effect sizes and statistical tests if needed.

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

This package provides functions for loading large (10M+ lines) CSV and other delimited files, similar to read.csv, but typically faster and using less memory than the standard R loader. While not entirely general, it covers many common use cases when the types of columns in the CSV file are known in advance. In addition, the package provides a class int64', which represents 64-bit integers exactly when reading from a file. The latter is useful when working with 64-bit integer identifiers exported from databases. The CSV file loader supports common column types including integer', double', string', and int64', leaving further type transformations to the user.

This package provides methods to deal with under sampling in ecological bipartite networks from Terry and Lewis (2020) Ecology <doi:10.1002/ecy.3047> Includes tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely missing links. Also includes simple functions to resample from observed networks to generate confidence intervals for common ecological network metrics.

This package provides a set of utilities for matching products in different classification codes used in international trade research. It supports concordance between the Harmonized System (HS0, HS1, HS2, HS3, HS4, HS5, HS combined), the Standard International Trade Classification (SITC1, SITC2, SITC3, SITC4), the North American Industry Classification System (NAICS combined), as well as the Broad Economic Categories (BEC), the International Standard of Industrial Classification (ISIC), and the Standard Industrial Classification (SIC). It also provides code nomenclature/descriptions look-up, Rauch classification look-up (via concordance to SITC2), and trade elasticity look-up (via concordance to HS0 or SITC3 codes).

Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.

Every research team have their own script for calculation of hemodynamic indexes. This package makes it possible to insert a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files.

An implementation of Conic Multivariate Adaptive Regression Splines (CMARS) in R. See Weber et al. (2011) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization, <DOI:10.1080/17415977.2011.624770>. It constructs models by using the terms obtained from the forward step of MARS and then estimates parameters by using Tikhonov regularization and conic quadratic optimization. It is possible to construct models for prediction and binary classification. It provides performance measures for the model developed. The package needs the optimisation software MOSEK <https://www.mosek.com/> to construct the models. Please follow the instructions in Rmosek for the installation.

Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

This package provides a collection of functions that have been developed to assist experimenter in modeling chemical degradation kinetic data. The selection of the appropriate degradation model and parameter estimation is carried out automatically as far as possible and is driven by a rigorous statistical interpretation of the results. The package integrates already available goodness-of-fit statistics for nonlinear models. In addition it allows data fitting with the nonlinear first-order multi-target (FOMT) model.

This package provides a novel visualization technique for plotting timestamped events on a 24-hour circular clock face. This is particularly useful for analyzing daily patterns, event clustering, and gaps in temporal data. The package also generalizes this approach to create cyclic charts for other periods, including weekly and monthly cycles, enabling effective event planning and pattern analysis across multiple time frames.

An educational package providing intuitive functions for calculating confidence intervals (CI) for various statistical parameters. Designed primarily for teaching and learning about statistical inference (particularly confidence intervals). Offers user-friendly wrappers around established methods for proportions, means, and bootstrap-based intervals. Integrates seamlessly with Tidyverse workflows, making it ideal for classroom demonstrations and student exercises.

Sample and cell filtering as well as visualisation of output metrics from Cell Ranger by Grace X.Y. Zheng et al. (2017) <doi:10.1038/ncomms14049>. CRMetrics allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. CRMetrics allows for easy removal of ambient RNA using SoupX by Matthew D Young and Sam Behjati (2020) <doi:10.1093/gigascience/giaa151> or CellBender by Stephen J Fleming et al. (2022) <doi:10.1101/791699>. Furthermore, it is possible to preprocess data using Pagoda2 by Nikolas Barkas et al. (2021) <https://github.com/kharchenkolab/pagoda2> or Seurat by Yuhan Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048> followed by embedding of cells using Conos by Nikolas Barkas et al. (2019) <doi:10.1038/s41592-019-0466-z>. Finally, doublets can be detected using scrublet by Samuel L. Wolock et al. (2019) <doi:10.1016/j.cels.2018.11.005> or DoubletDetection by Gayoso et al. (2020) <doi:10.5281/zenodo.2678041>. In the end, cells are filtered based on user input for use in downstream applications.

Bindings to Google's C++ library Compact Language Detector 2 (see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically detects over 80 languages in plain text or HTML. For mixed-language input it returns the top three detected languages and their approximate proportion of the total classified text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a cld3 package on CRAN which uses a neural network model instead.

This package provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) <doi: 10.18637/jss.v107.i02> for details.

Tests on properties of space-time covariance functions. Tests on symmetry, separability and for assessing different forms of non-separability are available. Moreover tests on some classes of covariance functions, such that the classes of product-sum models, Gneiting models and integrated product models have been provided. It is the companion R package to the papers of Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability and some classes of space-time covariance function models <doi:10.1007/s00477-017-1472-2> and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for selecting a class of space-time covariance functions <doi:10.18637/jss.v094.i01>.