We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level.

This package provides functions for tabulating and summarizing categorical, multiple response, ordinal, and continuous variables in R data frames. Makes it easy to create clear, structured summary tables, so you spend less time wrangling data and more time interpreting it.

This package provides a toolkit for common statistical analyses including descriptive statistics, Student's t-tests (one-sample, independent, and paired), one-way and two-way Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA), chi-square tests, correlation analysis, simple and multiple linear regression, logistic regression, and non-parametric tests (Mann-Whitney U, Wilcoxon Signed Rank, and Kruskal-Wallis). Each function automatically interprets results in plain English, reporting effect sizes, confidence intervals, and p-value interpretations. Post-hoc tests are automatically applied following significant results. A master function automatically detects the appropriate test based on the structure of the input data. Methods are based on Cohen, J. (1988) <doi:10.4324/9780203771587>, Tukey, J. W. (1949) <doi:10.2307/3001913>, and Shapiro and Wilk (1965) <doi:10.2307/2333709>.

SqueezeMeta is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (<https://github.com/jtamames/SqueezeMeta>). This package provides functions loading SqueezeMeta results into R, filtering them based on different criteria, and visualizing the results using basic plots. The SqueezeMeta project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as vegan or DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) <doi:10.1186/s12859-020-03703-2>.

This package provides a tool to calculate sky illuminance values (in lux) for both sun and moon. The model is a translation of the Fortran code by Janiczek and DeYoung (1987) <https://archive.org/details/DTIC_ADA182110>.

Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide.

This package provides a major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().

This package provides a way to call the functions in SUNDIALS C ODE solving library (<https://computing.llnl.gov/projects/sundials>). Currently the serial version of ODE solver, CVODE', sensitivity calculator CVODES and differential algebraic solver IDA from the SUNDIALS library are implemented. The package requires ODE to be written as an R or Rcpp function and does not require the SUNDIALS library to be installed on the local machine.

Efficient procedures for fitting and cross-validating the structurally-regularized time-dependent Cox models.

An index is created using a mathematical model that transforms multi-dimensional variables into a single value. These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices. For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986) <DOI:10.1080/01621459.1986.10478336>, Singh, D., Basak, P., Kumar, R. and Ahmad, T. (2023) <DOI:10.3389/fams.2023.1274530>.

Measures memory and CPU usage of R code by regularly taking snapshots of calls to the system command ps'. The package provides an entry point (albeit coarse) to profile usage of system resources by R code run in parallel.

This package provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See ?SpaDES.tools for an categorized overview of these additional tools. The suggested package NLMR can be installed from the following repository: (<https://PredictiveEcology.r-universe.dev>).

This package provides tools which allow regression variables to be placed on similar scales, offering computational benefits as well as easing interpretation of regression output.

This package provides helper functions to compute linear predictors, time-dependent ROC curves, and Harrell's concordance index for Cox proportional hazards models as described in Therneau (2024) <https://CRAN.R-project.org/package=survival>, Therneau and Grambsch (2000, ISBN:0-387-98784-3), Hung and Chiang (2010) <doi:10.1002/cjs.10046>, Uno et al. (2007) <doi:10.1198/016214507000000149>, Blanche, Dartigues, and Jacqmin-Gadda (2013) <doi:10.1002/sim.5958>, Blanche, Latouche, and Viallon (2013) <doi:10.1007/978-1-4614-8981-8_11>, Harrell et al. (1982) <doi:10.1001/jama.1982.03320430047030>, Peto and Peto (1972) <doi:10.2307/2344317>, Schemper (1992) <doi:10.2307/2349009>, and Uno et al. (2011) <doi:10.1002/sim.4154>.

Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.

Create a hexagon tile map display from spatial polygons. Each polygon is represented by a hexagon tile, placed as close to it's original centroid as possible, with a focus on maintaining spatial relationship to a focal point. Developed to aid visualisation and analysis of spatial distributions across Australia, which can be challenging due to the concentration of the population on the coast and wide open interior.

This package provides methods to calculate sample size for single-arm survival studies using the arcsine transformation, incorporating uniform accrual and exponential survival assumptions. Includes functionality for detailed numerical integration and simulation. This method is based on Nagashima et al. (2021) <doi:10.1002/pst.2090>.

Allows the user to animate shiny elements when scrolling to view them. The animations are activated using the scrollrevealjs library. See <https://scrollrevealjs.org/> for more information.

Provide a workflow to jointly embed chromatin accessibility peaks and expressed genes into a shared low-dimensional space using paired single-cell ATAC-seq (scATAC-seq) and single-cell RNA-seq (scRNA-seq) data. It integrates regulatory relationships among peak-peak interactions (via Cicero'), peak-gene interactions (via Lasso, random forest, and XGBoost), and gene-gene interactions (via principal component regression). With the input of paired scATAC-seq and scRNA-seq data matrices, it assigns a low-dimensional feature vector to each gene and peak. Additionally, it supports the reconstruction of gene-gene network with low-dimensional projections (via epsilon-NN) and then the comparison of the networks of two conditions through manifold alignment implemented in scTenifoldNet'. See <doi:10.1093/bioinformatics/btaf483> for more details.

Chat with large language models on your machine without internet with complete privacy via ollama', powered by R shiny interface. For more information on ollama', visit <https://ollama.com>.

This package provides functions for generating Standardized Climate Indices (SCI). Functions for generating Standardized Climate Indices (SCI). SCI is a transformation of (smoothed) climate (or environmental) time series that removes seasonality and forces the data to take values of the standard normal distribution. SCI was originally developed for precipitation. In this case it is known as the Standardized Precipitation Index (SPI).

The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators <doi:10.18637/jss.v107.i08>. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) <doi:10.1016/j.jocm.2023.100432>.

This package provides functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <doi:10.48550/arXiv.1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.

This package provides a framework for performing discrete (share-level) simulations of investment strategies. Simulated portfolios optimize exposure to an input signal subject to constraints such as position size and factor exposure. For background see L. Chincarini and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".