This package provides functions to aid in micro and macro economic analysis and handling of price and currency data. Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020).

This package provides functions to create confidence intervals for ratios of Poisson rates under misclassification using double sampling. Implementations of the methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016). "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way Differential Misclassification Using Double Sampling." Computational Statistics & Data Analysis, 95:122â 132.

This package provides functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as vcf files or rsync files generated by the the PoPoolation software) and perform conversion to alternative format (as used in the BayPass and SelEstim software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in TreeMix, BayPass and SelEstim format).

Compute standard Non-Compartmental Analysis (NCA) parameters for typical pharmacokinetic analyses and summarize them.

Fits heterogeneous panel data models with interactive effects for linear regression, logistic, count, probit, quantile, and clustering. Based on Ando, T. and Bai, J. (2015) "A simple new test for slope homogeneity in panel data models with interactive effects" <doi: 10.1016/j.econlet.2015.09.019>, Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" <doi: 10.1093/jjfinex/nbu026> , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" <doi: 10.1002/jae.2467>, Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" <doi: 10.1080/01621459.2016.1195743>, Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" <doi: 10.1080/01621459.2018.1543598>, Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" <doi: 10.1016/j.jeconom.2020.11.013.>.

Useful for preparing and cleaning data. It includes functions to center data, reverse coding, dummy code and effect code data, and more.

Data and examples from meta-analyses in psychology research.

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>).

This package provides adds postfix and infix logic operators for if, then, unless, and otherwise.

Read and write GraphPad Prism .pzfx files in R.

We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?

Includes JavaScript files that allow plotly maps to render without an internet connection.

Allows users to derive multi-objective weights from pairwise comparisons, which research shows is more repeatable, transparent, and intuitive other techniques. These weights can be rank existing alternatives or to define a multi-objective utility function for optimization.

This package infers the trends of one or several animal populations over time from series of counts. It does so by accounting for count precision (provided or inferred based on expert knowledge, e.g. guesstimates), smoothing the population rate of increase over time, and accounting for the maximum demographic potential of species. Inference is carried out in a Bayesian framework. This work is part of the FRB-CESAB working group AfroBioDrivers <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.

Set the R prompt dynamically, from a function. The package contains some examples to include various useful dynamic information in the prompt: the status of the last command (success or failure); the amount of memory allocated by the current R process; the name of the R package(s) loaded by pkgload and/or devtools'; various git information: the name of the active branch, whether it is dirty, if it needs pushes pulls. You can also create your own prompt if you don't like the predefined examples.

Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: data', a dataframe containing player data for any sports; player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; name_col', specifying the name of the column in the dataframe that contains the player names; and player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title.

This package implements two differentially private algorithms for estimating L2-regularized logistic regression coefficients. A randomized algorithm F is epsilon-differentially private (C. Dwork, Differential Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon for any pair D, D of datasets that differ in exactly one record, any measurable set S, and the randomness is taken over the choices F makes.

This package provides an interface to access public economic and financial data for economic research and quantitative analysis. The data sources including NBS, FRED, Sina, Eastmoney and etc. It also provides quantitative functions for trading strategies based on the data.table', TTR', PerformanceAnalytics and etc packages.

Power and sample size calculation for bulk tissue and single-cell eQTL analysis based on ANOVA, simple linear regression, or linear mixed effects model. It can also calculate power/sample size for testing the association of a SNP to a continuous type phenotype. Please see the reference: Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021) <doi:10.1093/bioinformatics/btab385>.

This package provides a collection of privacy-preserving distributed algorithms (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.

This package implements tools for the analysis of partially ordered data, with a particular focus on the evaluation of multidimensional systems of indicators and on the analysis of poverty. References, Fattore M. (2016) <doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016) <doi:10.1007/s11205-016-1501-4> Arcagni A. (2017) <doi:10.1007/978-3-319-45421-4_19>.

Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.

Validation of risk predictions obtained from survival models and competing risk models based on censored data using inverse weighting and cross-validation. Most of the pec functionality has been moved to riskRegression'.

This package provides a tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.