Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.

Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024).

Retrieval the leaf area index (LAI) and soil moisture (SM) from microwave backscattering data using water cloud model (WCM) model . The WCM algorithm attributed to Pervot et al.(1993) <doi:10.1016/0034-4257(93)90053-Z>. The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work.

This package implements a self-organizing map which has application in gene clustering. It provides functions like:

• filtering data by certain floor, ceiling, max/min ratio, and max - min difference;

• normalization of the data;

• get the average distortion measure;

• train a self-organizing map;

• summarize a som object;

• yeast cell cycle.

This package solves convex cone programs via operator splitting. It can solve: linear programs, second-order cone programs, semidefinite programs, exponential cone programs, and power cone programs, or problems with any combination of those cones. SCS uses AMD (a set of routines for permuting sparse matrices prior to factorization) and LDL (a sparse LDL factorization and solve package) from SuiteSparse.

Extend Rasch and Item Response Theory (IRT) analyses by providing tools for post-processing the output from five major IRT packages (i.e., eRm', psychotools', ltm', mirt', and TAM'). The current version provides the plotPIccc() function, which extracts from the return object of the originating package all information required to draw an extended Person-Item-Map (PIccc), showing any combination of * category characteristic curves (CCCs), * threshold characteristic curves (TCCs), * item characteristic curves (ICCs), * category information functions (CIFs), * item information functions (IIFs), * test information function (TIF), and the * standard error curve (S.E.). for uni- and multidimensional models (as far as supported by each package). It allows for selecting dimensions, items, and categories to plot and offers numerous options to adapt the output. The return object contains all calculated values for further processing.

Redox is a C++ interface to the Redis key-value store that makes it easy to write applications that are both elegant and high-performance. Communication should be a means to an end, not something we spend a lot of time worrying about. Redox takes care of the details so you can move on to the interesting part of your project.

An interface to the Bayesian Weighted Sums model implemented in RStan'. It estimates the summed effect of multiple, often moderately to highly correlated, continuous predictors. Its applications can be found in analysis of exposure mixtures. The model was proposed by Hamra, Maclehose, Croen, Kauffman, and Newschaffer (2021) <doi:10.3390/ijerph18041373>. This implementation includes an extension to model binary outcome.

Solves ordinary and delay differential equations, where the objective function is written in either R or C. Suitable only for non-stiff equations, the solver uses a Dormand-Prince method that allows interpolation of the solution at any point. This approach is as described by Hairer, Norsett and Wanner (1993) <ISBN:3540604529>. Support is also included for iterating difference equations.

The DER (or PaF) income polarization index as proposed by Duclos J. Y., Esteban, J. and Ray D. (2004). "Polarization: concepts, measurement, estimation". Econometrica, 72(6): 1737--1772. <doi:10.1111/j.1468-0262.2004.00552.x>. The index may be computed for a single or for a range of values of the alpha-parameter. Bootstrapping is also available.

Presents a "Scenarios" class containing general parameters, risk parameters and projection results. Risk parameters are gathered together into a ParamsScenarios sub-object. The general process for using this package is to set all needed parameters in a Scenarios object, use the customPathsGeneration method to proceed to the projection, then use xxx_PriceDistribution() methods to get asset prices.

An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the boot package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) <doi:10.1080/00031305.2020.1731599> for details.

This package provides a collection of functions to fit and explore single, multi-component and restricted Frequency Modulated Moebius (FMM) models. FMM is a nonlinear parametric regression model capable of fitting non-sinusoidal shapes in rhythmic patterns. Details about the mathematical formulation of FMM models can be found in Rueda et al. (2019) <doi:10.1038/s41598-019-54569-1>.

Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours). Documentation about gRc is provided in the paper by Hojsgaard and Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).

Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.

Local partial likelihood estimation by Fan, Lin and Zhou(2006)<doi:10.1214/009053605000000796> and simultaneous confidence band is a set of tools to test the covariates-biomarker interaction for survival data. Test for the covariates-biomarker interaction using the bootstrap method and the asymptotic method with simultaneous confidence band (Liu, Jiang and Chen (2015)<doi:10.1002/sim.6563>).

This package provides a word embeddings-based semi-supervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>. LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove). It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.

Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) <doi:10.1007/s13253-018-0321-1>. In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation.

This package provides methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework. For technical details on the MVS and stacked penalized logistic regression (StaPLR) methods see Van Loon, Fokkema, Szabo, & De Rooij (2020) <doi:10.1016/j.inffus.2020.03.007> and Van Loon et al. (2022) <doi:10.3389/fnins.2022.830630>.

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

This package provides a series of tools for analyzing Systems Factorial Technology data. This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions. Houpt, Blaha, McIntire, Havig, and Townsend (2013) <doi:10.3758/s13428-013-0377-3> provide a basic introduction to Systems Factorial Technology along with examples using the sft R package.

An implementation of equilibrium-based yield per recruit methods. Yield per recruit methods can used to estimate the optimal yield for a fish population as described by Walters and Martell (2004) <isbn:0-691-11544-3>. The yield can be based on the number of fish caught (or harvested) or biomass caught for all fish or just large (trophy) individuals.

Developed for Computing the probability density function, cumulative distribution function, random generation, estimating the parameters of asymmetric exponential power distribution, and robust regression analysis with error term that follows asymmetric exponential power distribution. The asymmetric exponential power distribution studied here is a special case of that introduced by Dongming and Zinde-Walsh (2009) <doi:10.1016/j.jeconom.2008.09.038>.

The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius. In addition to this, we also provide a simple way (Jones and Hulme, 1996) to grid the irregularly-spaced data points onto regular latitude-longitude grids by averaging all stations in grid-boxes.