For SaaS builders, software companies, ISVs and ISOs who want to embed payments into their tech stack
NMI Payments is an embedded payments solution that lets SaaS platforms, Software companies and ISVs integrate, brand, and manage payment acceptance directly within their software—without becoming a PayFac or building complex infrastructure. As a full-stack processor, acquirer, and technology partner, NMI handles onboarding, compliance, and risk so you can stay focused on growth. The modular, white-label platform supports omnichannel payments, from online, mobile and in-app to in-store and unattended. Choose from full-code, low-code, or no-code integration paths and launch in weeks, not months. Built-in risk tools, flexible monetization, and customizable branding help you scale faster while keeping full control of your experience. With NMI’s developer-first tools, sandbox testing, and modern APIs, you can embed payments quickly and confidently.
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Cloud data warehouse to power your data-driven innovation
BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.
BigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows you to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.
Scripts used to detect multiple optima of likelihood on real data.
R scripts and sequence data used in the paper "Multiple local maxima for likelihoods of phylogenetic trees constructed from biological data." by McComish BJ, Schliep KP and Penny D (submitted to Systematic Biology).
YANG (Yet Another Network Generator - Java) enables you to generate social networks given various social rules observed in the real population. Uses: generate realistic networks to be used in individual-centric models, teaching or benchmarking.