This package contains model fitting functions for linear and non-linear adsorption kinetic and diffusion models. Adsorption kinetics is used for characterizing the rate of solute adsorption and the time necessary for the adsorption process. Adsorption kinetics offers vital information on adsorption rate, adsorbent performance in response time, and mass transfer processes. In addition, diffusion models are included in the package as solute diffusion affects the adsorption kinetic experiments. This package consists of 20 adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo Second Order (PSO), Elovich, and Weber-Morris model (commonly called the intraparticle model) stated by Plazinski et al. (2009) <doi:10.1016/j.cis.2009.07.009>. This package also contains a summary function where the statistical errors of each model are ranked for a more straightforward determination of the best fit model.

This package performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level. A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) <doi:10.1142/S0218202501001264>. O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) <doi:10.1016/S0040-5809(02)00058-8>. A.M. de Roos (2008) <doi:10.1111/j.1461-0248.2007.01121.x>.

Spectral transmittance data for frequently used filters and similar materials. Plastic sheets and films; photography filters; theatrical gels; machine-vision filters; various types of window glass; optical glass and some laboratory plastics and glassware. Spectral reflectance data for frequently encountered materials. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis API is used by organizations such as Statistics Sweden and Statistics Finland to disseminate data. The R package can interact with all PX-Web/PC-Axis APIs to fetch information about the data hierarchy, extract metadata and extract and parse statistics to R data.frame format. PX-Web is a solution to disseminate PC-Axis data files in dynamic tables on the web. Since 2013 PX-Web contains an API to disseminate PC-Axis files.

Clustering analysis for sparse microbiome data, based on a Poisson hurdle model.

Enables user to perform the following: 1. Roll n number of die/dice (roll()). 2. Toss n number of coin(s) (toss()). 3. Play the game of Rock, Paper, Scissors. 4. Choose n number of card(s) from a pack of 52 playing cards (Joker optional).

This package provides functions to perform the peer performance analysis of funds returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.

Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the lavaan', 2)a sample of sequential path analysis in metan suggested by Olivoto and Lúcio (2020) <doi:10.1111/2041-210X.13384>, 3)the simple PATHSAS macro written in SAS by Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the semPlot() function of OpenMx as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a Minitab macro developed by Arminian, A. in the paper by Arminian et al. (2008) <doi:10.1080/15427520802043182>.

Procrustes analyses to infer co-phylogenetic matching between pairs of phylogenetic trees.

This package provides tools for performing disproportionality analysis using the information component, proportional reporting rate and the reporting odds ratio. The anticipated use is passing data to the da() function, which executes the disproportionality analysis. See Norén et al (2011) <doi:10.1177/0962280211403604> and Montastruc et al (2011) <doi:10.1111/j.1365-2125.2011.04037.x> for further details.

An implementation of the van Westendorp Price Sensitivity Meter in R, which is a survey-based approach to analyze consumer price preferences and sensitivity (van Westendorp 1976, isbn:9789283100386).

This package provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.

Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.

Color palettes generated from paintings.

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.

Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).

This package performs genomic prediction of hybrid performance using eight statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) <doi:10.1534/g3.116.038059>; Xu Y et al (2021) <doi: 10.1111/pbi.13458>).

The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3, are provided to compute the test statistics in two and three dimensional spaces, respectively. Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is a variant of the Peacock test.

This package provides functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability. The methods for Alpha and Beta irreplaceability were first described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping Global Irreplaceability. Conservation Biology 2021;1-11. <doi:10.1111/cobi.13806>.

Calculates the percentage coefficient of variation (CV) for mass spectrometry-based proteomic data. The CV can be calculated with the traditional formula for raw (non log transformed) intensity data, or log transformed data.

Market odds from from Pinnacle, an online sports betting bookmaker (see <https://www.pinnacle.com> for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. These datasets can be used to build models and compare statistical information with the information from prediction markets.The Major League Baseball (MLB) 2016 dataset can be used for sabermetrics analysis and also can be used in conjunction with other popular Major League Baseball (MLB) datasets such as Retrosheets or the Lahman package by merging by GameID.

The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with a prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions or Anders'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the dabestr (>=0.3.0) R package. Current v1.3.2 fixed bug with "Class" recognition and updated dabestr functions.

Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A multivariate nonparametric density estimator based on the maximum-entropy method. Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.

There are two main functions: (1) To estimate the power of testing for linkage using an affected sib pair design, as a function of the recurrence risk ratios. We will use analytical power formulae as implemented in R. These are based on a Mathematica notebook created by Martin Farrall. (2) To examine how the power of the transmission disequilibrium test (TDT) depends on the disease allele frequency, the marker allele frequency, the strength of the linkage disequilibrium, and the magnitude of the genetic effect. We will use an R program that implements the power formulae of Abel and Muller-Myhsok (1998). These formulae allow one to quickly compute power of the TDT approach under a variety of different conditions. This R program was modeled on Martin Farrall's Mathematica notebook.