An implementation of the "Design Analysis" proposed by Gelman and Carlin (2014) <doi:10.1177/1745691614551642>. It combines the evaluation of Power-Analysis with other inferential-risks as Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also Altoè et al. (2020) <doi:10.3389/fpsyg.2019.02893> and Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.

An API wrapper around the ProPublica API <https://projects.propublica.org/api-docs/congress-api/> for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the RPublica package because it is for the ProPublica U.S. Congress data API, and the RPublica package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs.

This package provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.

Given a SpatialPolygonsDataFrame and a set of populations for each polygon, compute a population density estimate based on Tobler's pycnophylactic interpolation algorithm. The result is a SpatialGridDataFrame. Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.

This package provides randomization using permutation for applications. To provide a Quality Control (QC) check, QC samples can be randomized within strata. A second function allows for the ability to â switchâ samples to meet set requirements and perform a certain amount of minimization on these switches. The functions are flexible for users by specifying strata size and number of QC samples per strata. The randomization meets the following requirements â ¢ QC sample requirements: QC samples not adjacent, QC samples from same mother must follow certain patterns. â ¢ Matched sample sets must be within a single strata, and next to each other.

Normalizes city names for Germany (DE) and Switzerland (CH) and matches them to NUTS 3 regions using provided crosswalks. Features include comprehensive normalization rules, cascading matching logic (Exact NUTS -> Exact LAU -> Fuzzy), and single-source data synthesis. The package implements the NUTS classification as described in the NUTS methodology (Eurostat (2021) <https://ec.europa.eu/eurostat/web/nuts>).

Screens and sorts phylogenetic trees in both traditional and extended Newick format. Allows for the fast and flexible screening (within a tree) of Exclusive clades that comprise only the target taxa and/or Non- Exclusive clades that includes a defined portion of non-target taxa.

An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.

Spatial Analysis for exploration of Pakistan Population Census 2017 (<https://www.pbs.gov.pk/content/population-census>). It uses data from R package PakPC2017'.

Aims at detecting single nucleotide variation (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used as a surrogate marker for tumor, at each base position of an Next Generation Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele frequency at each position to the measured PER in control samples.

An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.

Enforces good practice and provides convenience functions to make work with JavaScript not just easier but also scalable. It is a robust wrapper to NPM', yarn', and webpack that enables to compartmentalize JavaScript code, leverage NPM and yarn packages, include TypeScript', React', or Vue in web applications, and much more.

Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.

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.

Allows users to access the Oregon State Prism climate data (<https://prism.nacse.org/>). Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.

An R package for polygenic trait analysis.

Quickly and easily add a mini map to your rmarkdown html documents.

Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.

Read and write GraphPad Prism .pzfx files in R.

Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. pathfindR is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. pathfindR also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in pathfindR are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.

Global hypothesis tests combine information across multiple endpoints to test a single hypothesis. The prediction test is a recently proposed global hypothesis test with good performance for small sample sizes and many endpoints of interest. The test is also flexible in the types and combinations of expected results across the individual endpoints. This package provides functions for data processing and calculation of the prediction test.

Calculates various functions needed for design and monitoring survival trials accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual, and different censoring distributions between groups. The event time distribution is assumed to be piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution. As compared with Version 1.2.1, two more types of hybrid crossover are added. A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, density, hazard and cumulative hazard functions. Also, to generate the crossover-adjusted event time random variable, a more efficient algorithm is used and the output includes crossover indicators.

Some functions at the intersection of dplyr and purrr that formerly lived in purrr'.

Power and sample size calculation for testing fixed effect coefficients in multilevel linear mixed effect models with one or more than one independent populations. Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.