Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of the Marker and Time-to-event variables.

Plays the game of Snakes and Ladders and has tools for analyses. The tools included allow you to find the average moves to win, frequency of each square, importance of the snakes and the ladders, the most common square and the plotting of the game played.

This package performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.

This package provides methods for computing spatial, temporal, and spatiotemporal statistics as described in Gouhier and Guichard (2014) <doi:10.1111/2041-210X.12188>. These methods include empirical univariate, bivariate and multivariate variograms; fitting variogram models; phase locking and synchrony analysis; generating autocorrelated and cross-correlated matrices.

Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) <doi:10.1111/rssb.12499>. SLCARE returns estimates for non-functional model parameters along with the associated variance estimates and p-values. Visualization tools are provided to depict the estimated functional model parameters and related functional quantities of interest. SLCARE also delivers a model checking plot to help assess the adequacy of the fitted model.

Create in-app purchasing and subscriptions through Servicebot payment using the Stripe framework.

Implementations of stochastic, limited-memory quasi-Newton optimizers, similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, for smooth stochastic optimization. Implements the following methods: oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>). Provides functions for easily creating R objects with partial_fit/predict methods from some given objective/gradient/predict functions. Includes an example stochastic logistic regression using these optimizers. Provides header files and registered C routines for using it directly from C/C++.

This package provides functions to be used in conjunction with the Sequential package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the Sequential package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the Sequential package on these populations, which allows for the exploration of outcome misclassification in data.

This package provides functionality for simulating data generation processes across various spatial regression models, conceptually aligned with the dgp module of the Python library spreg <https://pysal.org/spreg/api.html#dgp>.

An interface to access data from Substack publications via API. Users can fetch the latest, top, search for specific posts, or retrieve a single post by its slug. This functionality is useful for developers and researchers looking to analyze Substack content or integrate it into their applications. For more information, visit the API documentation at <https://substackapi.dev/introduction>.

This package provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.

This package implements Surprisal analysis for gene expression data such as RNA-seq or microarray experiments. Surprisal analysis is an information-theoretic method that decomposes gene expression data into a baseline state and constraint-associated deviations, capturing coordinated gene expression patterns under different biological conditions. References: Kravchenko-Balasha N. et al. (2014) <doi:10.1371/journal.pone.0108549>. Zadran S. et al. (2014) <doi:10.1073/pnas.1414714111>. Su Y. et al. (2019) <doi:10.1371/journal.pcbi.1007034>. Bogaert K. A. et al. (2018) <doi:10.1371/journal.pone.0195142>.

Computes synchrony as windowed cross-correlation based on two-dimensional time series in a text file you can upload. SUSY works as described in Tschacher & Meier (2020) <doi:10.1080/10503307.2019.1612114>.

This package provides functions to speed up work flow for hydrological analysis. Focused on Australian climate data (SILO climate data), hydrological models (eWater Source) and in particular South Australia (<https://water.data.sa.gov.au> hydrological data).

An efficient tool for fitting nested mixture models based on a shared set of atoms via Markov Chain Monte Carlo and variational inference algorithms. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures with Gaussian kernels equipped with a normal-inverse gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>, Dâ Angelo, Denti (2024) <doi:10.1214/24-BA1458>.

Hail is an open-source, general-purpose, python based data analysis tool with additional data types and methods for working with genomic data, see <https://hail.is/>. Hail is built to scale and has first-class support for multi-dimensional structured data, like the genomic data in a genome-wide association study (GWAS). Hail is exposed as a python library, using primitives for distributed queries and linear algebra implemented in scala', spark', and increasingly C++'. The sparkhail is an R extension using sparklyr package. The idea is to help R users to use hail functionalities with the well-know tidyverse syntax, see <https://www.tidyverse.org/>.

This package provides SPSS- and SAS-like output for least squares multiple regression, logistic regression, and count variable regressions. Detailed output is also provided for OLS moderated regression, interaction plots, and Johnson-Neyman regions of significance. The output includes standardized coefficients, partial and semi-partial correlations, collinearity diagnostics, plots of residuals, and detailed information about simple slopes for interactions. The output for some functions includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo analyses. There are numerous options for model plots. The REGIONS_OF_SIGNIFICANCE function also provides Johnson-Neyman regions of significance and plots of interactions for both lm and lme models. There is also a function for partial and semipartial correlations and a function for conducting Cohen's set correlation analyses.

Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.

This package provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) <doi:10.1080/01621459.2014.957289>.

This package performs variable selection based on subsampling, ranking forward selection. Details of the method are published in Lihui Liu, Hong Gu, Johan Van Limbergen, Toby Kenney (2020) SuRF: A new method for sparse variable selection, with application in microbiome data analysis Statistics in Medicine 40 897-919 <doi:10.1002/sim.8809>. Xo is the matrix of predictor variables. y is the response variable. Currently only binary responses using logistic regression are supported. X is a matrix of additional predictors which should be scaled to have sum 1 prior to analysis. fold is the number of folds for cross-validation. Alpha is the parameter for the elastic net method used in the subsampling procedure: the default value of 1 corresponds to LASSO. prop is the proportion of variables to remove in the each subsample. weights indicates whether observations should be weighted by class size. When the class sizes are unbalanced, weighting observations can improve results. B is the number of subsamples to use for ranking the variables. C is the number of permutations to use for estimating the critical value of the null distribution. If the doParallel package is installed, the function can be run in parallel by setting ncores to the number of threads to use. If the default value of 1 is used, or if the doParallel package is not installed, the function does not run in parallel. display.progress indicates whether the function should display messages indicating its progress. family is a family variable for the glm() fitting. Note that the glmnet package does not permit the use of nonstandard link functions, so will always use the default link function. However, the glm() fitting will use the specified link. The default is binomial with logistic regression, because this is a common use case. pval is the p-value for inclusion of a variable in the model. Under the null case, the number of false positives will be geometrically distributed with this as probability of success, so if this parameter is set to p, the expected number of false positives should be p/(1-p).

Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field. Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) <doi: 10.1175/2009WAF2222269.1> and for a tutorial on some of the main functions available here, see Gilleland (2022) <doi: 10.5065/4px3-5a05>.

This package provides functions to perform robust variable selection and regression using the Fast and Scalable Cellwise-Robust Ensemble (FSCRE) algorithm. The approach establishes a robust foundation using the Detect Deviating Cells (DDC) algorithm and robust correlation estimates. It then employs a competitive ensemble architecture where a robust Least Angle Regression (LARS) engine proposes candidate variables and cross-validation arbitrates their assignment. A final robust MM-estimator is applied to the selected predictors.

An interactive shiny application to assist in determining sample sizes for common survey designs such as simple random sampling', stratified sampling', and cluster sampling'. It includes formulas, helper calculators, and illustrative examples.

This package provides an imputation pipeline for single-cell RNA sequencing data. The scISR method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran et.al. (2022) <DOI:10.1038/s41598-022-06500-4>).