Computes normalized cycle threshold (Ct) values (delta Ct) from raw quantitative polymerase chain reaction (qPCR) Ct values and conducts test of significance using t.test(). Plots expression values based from log2(2^(-1*delta delta Ct)) across groups per gene of interest. Methods for calculation of delta delta Ct and relative expression (2^(-1*delta delta Ct)) values are described in: Livak & Schmittgen, (2001) <doi:10.1006/meth.2001.1262>.

Code for centroid, median and quantile classifiers.

This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like EBS <https://ebs.excellenceinbreeding.org/>, BMS <https://bmspro.io>, BreedBase <https://breedbase.org>, GIGWA <https://github.com/SouthGreenPlatform/Gigwa2> (using BrAPI <https://brapi.org> calls), , and Germinate <https://germinateplatform.github.io/get-germinate/>. Extra helper functions support environmental data sources, including TerraClimate <https://www.climatologylab.org/terraclimate.html> and FAO HWSDv2 <https://gaez.fao.org/pages/hwsd> soil database.

Textual statistics functions formerly in the quanteda package. Textual statistics for characterizing and comparing textual data. Includes functions for measuring term and document frequency, the co-occurrence of words, similarity and distance between features and documents, feature entropy, keyword occurrence, readability, and lexical diversity. These functions extend the quanteda package and are specially designed for sparse textual data.

Example data used in package Qindex'.

Fit quantile regression neural network models with optional left censoring, partial monotonicity constraints, generalized additive model constraints, and the ability to fit multiple non-crossing quantile functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.

Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.

An R implementation of quality controlâ based robust LOESS(local polynomial regression fitting) signal correction for metabolomics data analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011) <doi:10.1038/nprot.2011.335>. The optimisation of LOESS's span parameter using generalized cross-validation (GCV) is provided as an option. In addition to signal correction, qcrlscR includes some utility functions like batch shifting and data filtering.

This package provides tools for an automated identification of diagnostic molecular characters, i.e. such columns in a given nucleotide or amino acid alignment that allow to distinguish taxa from each other. These characters can then be used to complement the formal descriptions of the taxa, which are often based on morphological and anatomical features. Especially for morphologically cryptic species, this will be helpful. QUIDDICH distinguishes between four different types of diagnostic characters. For more information, see "Kuehn, A.L., Haase, M. 2019. QUIDDICH: QUick IDentification of DIagnostic CHaracters.".

This package implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies. (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).

This package implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).

Dynamically generate tabset panels <https://quarto.org/docs/output-formats/html-basics.html#tabsets> in Quarto HTML documents using a data frame as input.

Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by ClinRisk'.

Extends the quadprog package to solve quadratic programs with absolute value constraints and absolute values in the objective function.

It provides versatile tools for analysis of birth and death based Markovian Queueing Models and Single and Multiclass Product-Form Queueing Networks. It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K, Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks, Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks and Single Channel Multiple Class Mixed Networks. Also it provides a B-Erlang, C-Erlang and Engset calculators. This work is dedicated to the memory of D. Sixto Rios Insua.

An implementation of two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. One index is based on a discrete choice model and the other is relying on an artificial neural network.

Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.

The QRI_func() function performs quantile regression analysis using age and sex as predictors to calculate the Quantile Regression Index (QRI) score for each individualâ s regional brain imaging metrics and then averages across the regional scores to generate an average tissue specific score for each subject. The QRI_plot() is used to plot QRI and generate the normative curves for individual measurements.

Datasets for the book, A Guide to QTL Mapping with R/qtl. Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.

Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to Vii7 software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).

Estimate quadratic vector autoregression models with the strong hierarchy using the Regularization Algorithm under Marginality Principle (RAMP) by Hao et al. (2018) <doi:10.1080/01621459.2016.1264956>, compare the performance with linear models, and construct networks with partial derivatives.

This package implements indices of qualitative variation proposed by Wilcox (1973).

Values different types of assets and calibrates discount curves for quantitative financial analysis. It covers fixed coupon assets, floating note assets, interest and cross currency swaps with different payment frequencies. Enables the calibration of spot, instantaneous forward and basis curves, making it a powerful tool for accurate and flexible bond valuation and curve generation. The valuation and calibration techniques presented here are consistent with industry standards and incorporates author's own calculations. Tuckman, B., Serrat, A. (2022, ISBN: 978-1-119-83555-4).

This package implements the Quantitative Classification-based on Association Rules (QCBA) algorithm (<doi:10.1007/s10489-022-04370-x>). QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. Supported are CBA implementations from rCBA', arulesCBA and arc packages, and CPAR', CMAR', FOIL2 and PRM implementations from arulesCBA package and SBRL implementation from the sbrl package. The result of the post-processing is an ordered CBA-like rule list.