An R6 class to set up, run, monitor, collate, and debug large simulation studies comprising many small independent replications and treatment configurations. Parallel processing, reproducibility, fault- and error-tolerance, and ability to resume an interrupted or timed-out simulation study are built in.

Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).

Spearman's rank correlation test with precomputed exact null distribution for n <= 22.

This package provides tools for reshaping, plotting, and manipulating matrices of orthogonal polynomials.

We consider the network structure detection for variables Y with auxiliary variables X accommodated, which are possibly subject to measurement error. The following three functions are designed to address various structures by different methods : one is NP_Graph() that is used for handling the nonlinear relationship between the responses and the covariates, another is Joint_Gaussian() that is used for correction in linear regression models via the Gaussian maximum likelihood, and the other Cond_Gaussian() is for linear regression models via conditional likelihood function.

This package provides a shiny app that allows to access and use the INVEKOS API for field polygons in Austria. API documentation is available at <https://gis.lfrz.gv.at/api/geodata/i009501/ogc/features/v1/>.

Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS.

This package provides functions and example datasets for phytosociological analysis, forest inventory, biomass and carbon estimation, and visualization of vegetation data. Includes functions to compute structural parameters [phytoparam(), summary.param(), stats()], estimate above-ground biomass and carbon [AGB()], stratify wood volume by diameter at breast height (DBH) classes [stratvol()], generate collector and rarefaction curves [collector.curve(), rarefaction()], and visualize basal areas on quadrat maps [BAplot(), including rectangular plots and individual coordinates]. Several example datasets are provided to demonstrate the functionality of these tools. For more details see FAO (1981, ISBN:92-5-101132-X) "Manual of forest inventory", IBGE (2012, ISBN:9788524042720) "Manual técnico da vegetação brasileira" and Heringer et al. (2020) "Phytosociology in R: A routine to estimate phytosociological parameters" <doi:10.22533/at.ed.3552009033>.

This package contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.

This package provides a broad-view perspective on data via linear mapping of data onto a radial coordinate system. The package contains functions to visualize the residual values of linear regression and Cartesian data in the defined radial scheme. See the pacviz documentation page for more information: <https://pacviz.sriley.dev/>.

This package provides methods to calculate and present PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA (<https://www.fva-bw.de/en/homepage/>) Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria.

Engineered features and "helper" functions ancillary to the public.ctn0094data package, extending this package for ease of use (see <https://CRAN.R-project.org/package=public.ctn0094data>). This public.ctn0094data package contains harmonized datasets from some of the National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN) projects. Specifically, the CTN-0094 project is to harmonize and de-identify clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for opioid use disorder. This current version is built from public.ctn0094data v. 1.0.6.

Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information.

Algorithms to speed up the Bayesian Lasso Cox model (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and the Bayesian Lasso Cox with mandatory variables (Zucknick et al. Biometrical J, 2015 <doi:10.1002/bimj.201400160>).

Power estimation and sample size calculation for 10X Visium Spatial Transcriptomics data to detect differential expressed genes between two conditions based on bootstrap resampling. See Shui et al. (2025) <doi:10.1371/journal.pcbi.1013293> for method details.

Generation of multiple count, binary and ordinal variables simultaneously given the marginal characteristics and association structure. Throughout the package, the word Poisson is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.

ProTracker is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play ProTracker module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of ProTracker and the Commodore Amiga alive. This package is the successor of ProTrackR providing better performance.

Compiles functions to trim, bin, visualise, and analyse activity/sleep time-series data collected from the Drosophila Activity Monitor (DAM) system (Trikinetics, USA). The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) <doi:10.1016/0022-5193(78)90022-X>, Lomb-Scargle periodogram: Lomb (1976) <doi:10.1007/BF00648343>, Scargle (1982) <doi:10.1086/160554> and Ruf (1999) <doi:10.1076/brhm.30.2.178.1422>, and Autocorrelation: Eijzenbach et al. (1986) <doi:10.1111/j.1440-1681.1986.tb00943.x>. Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) <doi:10.1021/ac60214a047>) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) <doi:10.1016/j.cub.2020.04.025>, Harrisingh method: Harrisingh et al. (2007) <doi:10.1523/JNEUROSCI.3680-07.2007>, and Stoleru method: Stoleru et al. (2004) <doi:10.1038/nature02926>. Rose plots and circular analysis are based on methods from - Batschelet (1981) <ISBN:0120810506> and Zar (2010) <ISBN:0321656865>.

This extension of the poems pattern-oriented modeling (POM) framework provides a collection of modules and functions customized for paleontological time-scales, and optimized for single-generation transitions and large populations, across multiple generations.

Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.

Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>.

The function pointdensity returns a density count and the temporal average for every point in the original list. The dataframe returned includes four columns: lat, lon, count, and date_avg. The "lat" column is the original latitude data; the "lon" column is the original longitude data; the "count" is the density count of the number of points within a radius of radius*grid_size (the neighborhood); and the date_avg column includes the average date of each point in the neighborhood.

Calculates, via simulation, power and appropriate stopping alpha boundaries (and/or futility bounds) for sequential analyses (i.e., group sequential design) as well as for multiple hypotheses (multiple tests included in an analysis), given any specified global error rate. This enables the sequential use of practically any significance test, as long as the underlying data can be simulated in advance to a reasonable approximation. Lukács (2022) <doi:10.21105/joss.04643>.

Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.