Self-Consistent Field(SCF) calculation method is one of the most important steps in the calculation methods of quantum chemistry. Ehrenreich, H., & Cohen, M. H. (1959). <doi:10.1103/PhysRev.115.786> However, the most prevailing software in this area, Gaussian''s SCF convergence process is hard to monitor, especially while the job is still running, causing researchers difficulty in knowing whether the oscillation has started or not, wasting time and energy on useless configurations or abandoning the jobs that can actually work. M.J. Frisch, G.W. Trucks, H.B. Schlegel et al. (2016). <https://gaussian.com> SCFMonitor enables Gaussian quantum chemistry calculation software users to easily read the Gaussian .log files and monitor the SCF convergence and geometry optimization process with little effort and clear, beautiful, and clean outputs. It can generate graphs using tidyverse to let users check SCF convergence and geometry optimization processes in real-time. The software supports processing .log files remotely using with rbase::url(). This software is a suitcase for saving time and energy for the researchers, supporting multiple versions of Gaussian'.

Health research using data from electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error for association tests. Here, the assumed target of inference is the relationship between binary disease status and predictors modeled using a logistic regression model. SAMBA implements several methods for obtaining bias-corrected point estimates along with valid standard errors as proposed in Beesley and Mukherjee (2020) <doi:10.1111/biom.13400>, Biometrics.

Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) <arXiv:2002.05746>. This package provides methods for fitting ITS models with lagged outcomes and variables to account for temporal dependencies. It then conducts inference via simulation, simulating a set of plausible counterfactual post-policy series to compare to the observed post-policy series. This package also provides methods to visualize such data, and also to incorporate seasonality models and smoothing and aggregation/summarization. This work partially funded by Arnold Ventures in collaboration with MDRC.

Preview spatial data as leaflet maps with minimal effort. smartmap is optimized for interactive use and distinguishes itself from similar packages because it does not need real spatial ('sp or sf') objects an input; instead, it tries to automatically coerce everything that looks like spatial data to sf objects or leaflet maps. It - for example - supports direct mapping of: a vector containing a single coordinate pair, a two column matrix, a data.frame with longitude and latitude columns, or the path or URL to a (possibly compressed) shapefile'.

This package provides a pipeline for the comparative analysis of collective movement data (e.g. fish schools, bird flocks, baboon troops) by processing 2-dimensional positional data (x,y,t) from GPS trackers or computer vision tracking systems, discretizing events of collective motion, calculating a set of established metrics that characterize each event, and placing the events in a multi-dimensional swarm space constructed from these metrics. The swarm space concept, the metrics and data sets included are described in: Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King Andrew J. (2023) <doi:10.1098/rstb.2022.0068>.

This package provides methods for regression with high-dimensional predictors and univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.

It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes the solutions to the model, plots of the solution, a summary of the features of the model, a function that covers different types of consumption preferences, and a function that computes the moments of a Markov process. Merton, Robert C (1971) <doi:10.1016/0022-0531(71)90038-X>, Tauchen, George (1986) <doi:10.1016/0165-1765(86)90168-0>, Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ).

Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Denti (2024) <doi:10.1214/24-BA1458>.

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>.

This package implements stacked elastic net regression (Rauschenberger 2021 <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005 <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).

This package provides a set of tools to assist statistical programmers in validating Study Data Tabulation Model (SDTM) domain data sets. Statistical programmers are required to validate that a SDTM data set domain has been programmed correctly, per the SDTM Implementation Guide (SDTMIG) by CDISC (<https://www.cdisc.org/standards/foundational/sdtmig>), study specification, and study protocol using a process called double programming. Double programming involves two different programmers independently converting the raw electronic data cut (EDC) data into a SDTM domain data table and comparing their results to ensure accurate standardization of the data. One of these attempts is termed production and the other validation'. Generally, production runs are the official programs for submittals and these are written in SAS'. Validation runs can be programmed in another language, in this case R'.

Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.

This package provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like CellChat <https://github.com/jinworks/CellChat>, CellPhoneDB <https://www.cellphonedb.org/>, Cellinker <https://www.rna-society.org/cellinker/>, ICELLNET <https://github.com/soumelis-lab/ICELLNET>, and ConnectomeDB <https://humanconnectome.org/software/connectomedb/> to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales.

Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) <doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.

Enables the complete removal of various Shiny components, such as inputs, outputs and modules. It also aids in the removal of observers that have been created in dynamically created modules.

High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). Currently supports frailty data analysis. Allows for Weibull and Exponential distribution. Includes function for interval censored data.

This package provides an R client for Statistics Canada's Web Data Service. Users can describe the data they need in natural language, search the official table catalogue, and download complete data tables in English or French as data frames. Tables formerly known as CANSIM tables are identified by Product IDs. Warin (2024) <doi:10.5070/T5.1868>.

Is designed to interactively and reproducibly visualize and filter SNP (single-nucleotide polymorphism) datasets. This R-based implementation of SNP and genotype filters facilitates an interactive and iterative SNP filtering pipeline, which can be documented reproducibly via rmarkdown'. SNPfiltR contains functions for visualizing various quality and missing data metrics for a SNP dataset, and then filtering the dataset based on user specified cutoffs. All functions take vcfR objects as input, which can easily be generated by reading standard vcf (variant call format) files into R using the R package vcfR authored by Knaus and Grünwald (2017) <doi:10.1111/1755-0998.12549>. Each SNPfiltR function can return a newly filtered vcfR object, which can then be written to a local directory in standard vcf format using the vcfR package, for downstream population genetic and phylogenetic analyses.

Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder TMB in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in TMB by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) <doi:10.48550/arXiv.2110.07051>.

Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.

This package provides a time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. SPORTSCausal (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.

This package provides functions for small area estimation.

An user-friendly framework to preprocess raw item scores of questionnaires into factors or scores and standardize them. Standardization can be made either by their normalization in representative sample, or by import of premade scoring table.

This package provides functions connecting to the Salesforce Platform APIs (REST, SOAP, Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) <https://trailhead.salesforce.com/content/learn/modules/api_basics/api_basics_overview>. "API" is an acronym for "application programming interface". Most all calls from these APIs are supported as they use CSV, XML or JSON data that can be parsed into R data structures. For more details please see the Salesforce API documentation and this package's website <https://stevenmmortimer.github.io/salesforcer/> for more information, documentation, and examples.