Contrast analysis for factorial designs provides an alternative to the traditional ANOVA approach, offering the distinct advantage of testing targeted hypotheses. The foundation of this package is primarily rooted in the works of Rosenthal, Rosnow, and Rubin (2000, ISBN: 978-0521659802) as well as Sedlmeier and Renkewitz (2018, ISBN: 978-3868943214).

Use the high-precision arithmetic provided by the R package Rmpfr to compute a custom-made Gauss quadrature nodes and weights, with up to 33 nodes, using a moment-based method via moment determinants. Paul Kabaila (2022) <arXiv:2211.04729>.

This package provides Capital Budgeting Analysis functionality and the essential Annuity loan functions. Also computes Loan Amortization Schedules including schedules with irregular payments.

This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <https://CRAN.R-project.org/package=survival>, Noah Simon et al. (2011) <doi:10.18637/jss.v039.i05>, Philippe Bastien et al. (2005) <doi:10.1016/j.csda.2004.02.005>, Philippe Bastien (2008) <doi:10.1016/j.chemolab.2007.09.009>, Philippe Bastien et al. (2014) <doi:10.1093/bioinformatics/btu660>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>, Florian Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>.

Method to implement some newly developed methods for the estimation of the conditional survival function. See Meira-Machado, Sestelo and Goncalves (2016) <doi:10.1002/bimj.201500038>.

Perceptually uniform palettes for commonly used variables in oceanography as functions taking an integer and producing character vectors of colours. See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M. and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for the guidelines adhered to when creating the palettes.

Calculate agrometeorological variables for crops including growing degree days (McMaster, GS & Wilhelm, WW (1997) <doi:10.1016/S0168-1923(97)00027-0>), cumulative rainfall, number of stress days and cumulative or mean radiation and evaporation. Convert dates to day of year and vice versa. Also, download curated and interpolated Australian weather data from the Queensland Government DES longpaddock website <https://www.longpaddock.qld.gov.au/>. This data is freely available under the Creative Commons 4.0 licence.

Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files.

The vctrs package provides a concept of vector prototype that can be especially useful when deploying models and code. Serialize these object prototypes to JSON so they can be used to check and coerce data in production systems, and deserialize JSON back to the correct object prototypes.

This package produces statistical indicators of the impact of migration on the socio-demographic composition of an area. Three measures can be used: ratios, percentages and the Duncan index of dissimilarity. The input data files are assumed to be in an origin-destination matrix format, with each cell representing a flow count between an origin and a destination area. Columns are expected to represent origins, and rows are expected to represent destinations. The first row and column are assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018) <doi:10.1080/00324728.2017.1416155> for technical details.

Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error. Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.

The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.

This package implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data.

In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2024). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. <doi:10.5705/ss.202020.0440>. In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.<doi:10.1016/j.csda.2022.107642>.

Engines for survival models from the parsnip package. These include parametric models (e.g., Jackson (2016) <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g., Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).

This package provides a framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time.

Based on individual market shares of all participants in a market or space, the package offers a set of different structural and concentration measures frequently - and not so frequently - used in research and in practice. Measures can be calculated in groups or individually. The calculated measure or the resulting vector in table format should help practitioners make more informed decisions. Methods used in this package are from: 1. Chang, E. J., Guerra, S. M., de Souza Penaloza, R. A. & Tabak, B. M. (2005) "Banking concentration: the Brazilian case". 2. Cobham, A. and A. Summer (2013). "Is It All About the Tails? The Palma Measure of Income Inequality". 3. Garcia Alba Idunate, P. (1994). "Un Indice de dominancia para el analisis de la estructura de los mercados". 4. Ginevicius, R. and S. Cirba (2009). "Additive measurement of market concentration" <doi:10.3846/1611-1699.2009.10.191-198>. 5. Herfindahl, O. C. (1950), "Concentration in the steel industry" (PhD thesis). 6. Hirschmann, A. O. (1945), "National power and structure of foreign trade". 7. Melnik, A., O. Shy, and R. Stenbacka (2008), "Assessing market dominance" <doi:10.1016/j.jebo.2008.03.010>. 8. Palma, J. G. (2006). "Globalizing Inequality: Centrifugal and Centripetal Forces at Work". 9. Shannon, C. E. (1948). "A Mathematical Theory of Communication". 10. Simpson, E. H. (1949). "Measurement of Diversity" <doi:10.1038/163688a0>.

Gain access to the Spark Catalog API making use of the sparklyr API. Catalog <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).

Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.

This package provides functions for classical test theory analysis, following methods presented by Wu et al. (2006) <doi:10.1007/978-981-10-3302-5>.

Reading and writing of files in the most commonly used formats of structural crystallography. It includes functions to work with a variety of statistics used in this field and functions to perform basic crystallographic computing. References: D. G. Waterman, J. Foadi, G. Evans (2011) <doi:10.1107/S0108767311084303>.

Package for CShapes 2.0, a GIS dataset of country borders (1886-today). Includes functions for data extraction and the computation of distance matrices and -lists.

Interface to interest and foreign exchange rates published by the Czech National Bank.

This package provides a set of functions to fit a boosting conditional logit model.