Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.

Enables correction for technical variance in raw quantitative reverse transcription polymerase chain reaction (qRT-PCR) data using the least squares-based NORMAgene data-driven normalization algorithm originally described by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250>. Performs normalization of raw crossing threshold values (CT) and also calculates relative variability metrics that can be used to assess the impact of normalization on variance.

Allele frequency databases for 50 forensic short tandem repeat (STR) markers, covering Norway and several broader regional populations: Europe, Africa, South America, West Asia, Middle Asia, and East Asia. Developed and maintained for use at the Department of Forensic Sciences, Oslo, Norway.

Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.

Calculation of molecular number and brightness from fluorescence microscopy image series. The software was published in a 2016 paper <doi:10.1093/bioinformatics/btx434>. The seminal paper for the technique is Digman et al. 2008 <doi:10.1529/biophysj.107.114645>. A review of the technique was published in 2017 <doi:10.1016/j.ymeth.2017.12.001>.

Get or set UNIX priority (niceness) of running R process.

This package provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, through the use of Stan and glmmTMB'. Also provides tools for model checking In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2023) <doi:10.1080/01621459.2023.2165929>, Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>, Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.

R binding over the nirs4all-datasets C ABI ('n4ds_*'): resolve a dataset id from the distributable catalog index into a version-pinned download contract, fetch the canonical Parquet ('Dataverse / Zenodo / figshare') with SHA-256 verification into a local cache, and re-verify a cached directory offline. JSON crosses the stable C ABI'; analysis of the data is left to the host.

This package provides R native ONNX model inference without requiring Python', reticulate bindings, or TensorFlow'. This package directly binds the ONNX Runtime C API via Rcpp', enabling real-time inference for .onnx engines, all within R. Standard CPU execution is supported as well as the CoreML Execution Provider (CEP) for Apple Silicon, all without external bindings. This package handles OS detection, linking ONNX libraries, and inference. For more information about ONNX Runtime see <https://onnxruntime.ai/>.

Calculates a cumulative summation nonparametric extended median test based on the work of Brown & Schaffer (2020) <DOI:10.1080/03610926.2020.1738492>. It then generates a control chart to assess processes and determine if any streams are out of control.

This package performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.

Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.

Draw nested extreme value random variables, which are the variables that appear in the latent variable formulation of the nested logit model.

Access and manipulation of data using the Neotoma Paleoecology Database. <https://api.neotomadb.org/api-docs/>. Examples in functions that require API access are not executed during CRAN checks. Vignettes do not execute as to avoid API calls during CRAN checks.

Color palettes for data visualization inspired by National Parks. Currently contains 15 color schemes and checks for colorblind-friendliness of palettes.

This package provides functions and datasets to support the book by Galecki and Burzykowski (2013), Linear Mixed-Effects Models: A Step-by-Step Approach', Springer. Includes functions for power calculations, log-likelihood contributions, and data simulation for linear mixed-effects models.

The Needleman-Wunsch global alignment algorithm can be used to find approximate matches between sample names in different data sets. See Wang et al. (2010) <doi:10.4137/CIN.S5613>.

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.

Lightweight interface to the OpenStreetMap Nominatim API <https://nominatim.org/release-docs/latest/>. Extract coordinates from addresses, retrieve addresses from coordinates, look up amenities and addresses, and return results as tibble or sf objects.

This package provides a unified, programmatic interface for searching, browsing, and retrieving metadata from various international organization data repositories that use the National Data Archive ('NADA') software, such as the World Bank, FAO', and the International Household Survey Network ('IHSN'). Functions allow users to discover available data collections, country codes, and access types, perform complex searches using keyword and spatial/temporal filters, and retrieve detailed study information, including file lists and variable-level data dictionaries. It simplifies access to microdata for researchers and policy analysts globally.

This package provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon igraph'.

Estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods, including robust bias-corrected confidence intervals as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>). The package includes lprobust() for local polynomial point estimation and robust bias-corrected inference, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).

Interface to gather news from the News API', based on a multilevel query <https://newsapi.org/>. A personal API key is required.

This package provides a collection of data structures that represent volumetric brain imaging data. The focus is on basic data handling for 3D and 4D neuroimaging data. In addition, there are function to read and write NIFTI files and limited support for reading AFNI files.