Scene Graph - Custom Geometry
The custom geometry example shows how to create a QQuickItem which uses the scene graph API to build a custom geometry for the scene graph. It does this by creating a BezierCurve item which is made part of the CustomGeometry module and makes use of this in a QML file.
BezierCurve Declaration
#include <QtQuick/QQuickItem> class BezierCurve : public QQuickItem { Q_OBJECT Q_PROPERTY(QPointF p1 READ p1 WRITE setP1 NOTIFY p1Changed) Q_PROPERTY(QPointF p2 READ p2 WRITE setP2 NOTIFY p2Changed) Q_PROPERTY(QPointF p3 READ p3 WRITE setP3 NOTIFY p3Changed) Q_PROPERTY(QPointF p4 READ p4 WRITE setP4 NOTIFY p4Changed) Q_PROPERTY(int segmentCount READ segmentCount WRITE setSegmentCount NOTIFY segmentCountChanged) public: BezierCurve(QQuickItem *parent = 0); ~BezierCurve(); QSGNode *updatePaintNode(QSGNode *, UpdatePaintNodeData *); QPointF p1() const { return m_p1; } QPointF p2() const { return m_p2; } QPointF p3() const { return m_p3; } QPointF p4() const { return m_p4; } int segmentCount() const { return m_segmentCount; } void setP1(const QPointF &p); void setP2(const QPointF &p); void setP3(const QPointF &p); void setP4(const QPointF &p); void setSegmentCount(int count); signals: void p1Changed(const QPointF &p); void p2Changed(const QPointF &p); void p3Changed(const QPointF &p); void p4Changed(const QPointF &p); void segmentCountChanged(int count); private: QPointF m_p1; QPointF m_p2; QPointF m_p3; QPointF m_p4; int m_segmentCount; };
The item declaration subclasses the QQuickItem class and adds five properties. One for each of the four control points in the bezier curve and a parameter to control the number of segments the curve is subdivided into. For each of the properties we have corresponding getter and setter functions. Since these properties can be bound to in QML, it is also preferable to have notifier signals for each of them so changes will be picked up the QML engine and used accordingly.
QSGNode *updatePaintNode(QSGNode *, UpdatePaintNodeData *);
The synchronization point between the QML scene and the rendering scene graph is the virtual function QQuickItem::updatePaintNode() which all items with custom scene graph logic must implement.
Note: The scene graph will on many hardware configurations be rendering on a separate thread. It is therefore crucial that interaction with the scene graph happens in a controlled manner, first and foremost through the QQuickItem::updatePaintNode() function.
BezierCurve Implementation
BezierCurve::BezierCurve(QQuickItem *parent) : QQuickItem(parent) , m_p1(0, 0) , m_p2(1, 0) , m_p3(0, 1) , m_p4(1, 1) , m_segmentCount(32) { setFlag(ItemHasContents, true); }
The BezierCurve constructor sets up default values for the control points and the number of segments. The bezier curve is specified in normalized coordinates relative to the item's bounding rectangle.
The constructor also sets the flag QQuickItem::ItemHasContents. This flags tells the canvas that this item provides visual content and will call QQuickItem::updatePaintNode() when it is time for the QML scene to be synchronized with the rendering scene graph.
BezierCurve::~BezierCurve() { }
The BezierCurve class has no data members that need to be cleaned up so the destructor does nothing. It is worth mentioning that the rendering scene graph is managed by the scene graph it self, potentially in a different thread, so one should never retain QSGNode references in the QQuickItem class nor try to clean them up explicitly.
void BezierCurve::setP1(const QPointF &p) { if (p == m_p1) return; m_p1 = p; emit p1Changed(p); update(); }
The setter function for the p1 property checks if the value is unchanged and exits early if this is the case. Then it updates the internal value and emits the changed signal. It then proceeds to call the QQuickItem::update() function which will notify the rendering scene graph, that the state of this object has changed and needs to be synchronized with the rendering scene graph. A call to update() will result in a call to QQuickItem::updatePaintNode() at a later time.
The other property setters are equivalent, and are omitted from this example.
QSGNode *BezierCurve::updatePaintNode(QSGNode *oldNode, UpdatePaintNodeData *) { QSGGeometryNode *node = 0; QSGGeometry *geometry = 0; if (!oldNode) { node = new QSGGeometryNode;
The updatePaintNode() function is the primary integration point for synchronizing the state of the QML scene with the rendering scene graph. The function gets passed a QSGNode which is the instance that was returned on the last call to the function. It will be null the first time the function gets called and we create our QSGGeometryNode which we will fill with geometry and a material.
geometry = new QSGGeometry(QSGGeometry::defaultAttributes_Point2D(), m_segmentCount); geometry->setLineWidth(2); geometry->setDrawingMode(QSGGeometry::DrawLineStrip); node->setGeometry(geometry); node->setFlag(QSGNode::OwnsGeometry);
We then create the geometry and add it to the node. The first argument to the QSGGeometry constructor is a definition of the vertex type, called an "attribute set". Since the graphics often used in QML centers around a few common standard attribute sets, these are provided by default. Here we use the Point2D attribute set which has two floats, one for x coordinates and one for y coordinates. The second argument is the vertex count.
Custom attribute sets can also created, but that is not covered in this example.
Since we do not have any special needs for memory managing the geometry, we specify that the QSGGeometryNode should own the geometry.
To minimize allocations, reduce memory fragmentation and improve performance, it would also be possible to make the geometry a member of a QSGGeometryNode subclass, in which case, we would not have set the QSGGeometryNode::OwnsGeometry flag.
QSGFlatColorMaterial *material = new QSGFlatColorMaterial; material->setColor(QColor(255, 0, 0)); node->setMaterial(material); node->setFlag(QSGNode::OwnsMaterial);
The scene graph API provides a few commonly used material implementations. In this example we use the QSGFlatColorMaterial which will fill the shape defined by the geometry with a solid color. Again we pass the ownership of the material to the node, so it can be cleaned up by the scene graph.
} else { node = static_cast<QSGGeometryNode *>(oldNode); geometry = node->geometry(); geometry->allocate(m_segmentCount); }
In the case where the QML item has changed and we only want to modify the existing node's geometry, we cast the oldNode
to a QSGGeometryNode instance and extract it's geometry. In case the segment count has changed, we call QSGGeometry::allocate() to make sure it has the right number of vertices.
QRectF bounds = boundingRect(); QSGGeometry::Point2D *vertices = geometry->vertexDataAsPoint2D(); for (int i = 0; i < m_segmentCount; ++i) { qreal t = i / qreal(m_segmentCount - 1); qreal invt = 1 - t; QPointF pos = invt * invt * invt * m_p1 + 3 * invt * invt * t * m_p2 + 3 * invt * t * t * m_p3 + t * t * t * m_p4; float x = bounds.x() + pos.x() * bounds.width(); float y = bounds.y() + pos.y() * bounds.height(); vertices[i].set(x, y); } node->markDirty(QSGNode::DirtyGeometry);
To fill the geometry, we first extract the vertex array from it. Since we are using one of the default attribute sets, we can use the convenience function QSGGeometry::vertexDataAsPoint2D(). Then we go through each segment and calculate its position and write that value to the vertex.
return node;
}
In the end of the function, we return the node so the scene graph can render it.
Application Entry-Point
int main(int argc, char **argv) { QGuiApplication app(argc, argv); qmlRegisterType<BezierCurve>("CustomGeometry", 1, 0, "BezierCurve"); QQuickView view; QSurfaceFormat format = view.format(); format.setSamples(16); view.setFormat(format); view.setSource(QUrl("qrc:///scenegraph/customgeometry/main.qml")); view.show(); app.exec(); }
The application is a straightforward QML application, with a QGuiApplication and a QQuickView that we pass a .qml file. To make use of the BezierCurve item, we need to register it in the QML engine, using the qmlRegisterType() function. We give it the name BezierCurve and make it part of the CustomGeometry 1.0
module.
As the bezier curve is drawn using GL_LINE_STRIP, we specify that the view should be multisampled to get antialiasing. This is not required, but it will make the item look a bit nicer on hardware that supports it. Multisampling is not enabled by default because it often results in higher memory usage.
Using the Item
import QtQuick 2.0 import CustomGeometry 1.0
Our .qml file imports the QtQuick 2.0
module to get the standard types and also our own CustomGeometry 1.0
module which contains our newly created BezierCurve objects.
Item { width: 300 height: 200 BezierCurve { id: line anchors.fill: parent anchors.margins: 20
Then we create the our root item and an instance of the BezierCurve which we anchor to fill the root.
property real t SequentialAnimation on t { NumberAnimation { to: 1; duration: 2000; easing.type: Easing.InOutQuad } NumberAnimation { to: 0; duration: 2000; easing.type: Easing.InOutQuad } loops: Animation.Infinite } p2: Qt.point(t, 1 - t) p3: Qt.point(1 - t, t) }
To make the example a bit more interesting we add an animation to change the two control points in the curve. The end points stay unchanged.
Text { anchors.bottom: line.bottom x: 20 width: parent.width - 40 wrapMode: Text.WordWrap text: "This curve is a custom scene graph item, implemented using GL_LINE_STRIP" } }
Finally we overlay a short text outlining what the example shows.
Files:


| autonumericInput {shinyWidgets} | R Documentation |
An R
wrapper over the javascript AutoNumeric
library, for
formatting numeric inputs in shiny applications.
autonumericInput( inputId, label, value, width = NULL, align = "center", currencySymbol = NULL, currencySymbolPlacement = NULL, decimalCharacter = NULL, digitGroupSeparator = NULL, allowDecimalPadding = NULL, decimalPlaces = NULL, divisorWhenUnfocused = NULL, rawValueDivisor = NULL, formatOnPageLoad = NULL, maximumValue = NULL, minimumValue = NULL, modifyValueOnWheel = NULL, ... )
inputId |
The |
label |
Display label for the control, or NULL for no label. |
value |
Initial value (unformatted). |
width |
The width of the input box, eg. |
align |
The alignment of the text inside the input box, one of "center" (default), "left", "right". |
currencySymbol |
Defines the currency symbol string. It can be a string of more than one character (allowing for instance to use a space on either side of it, example: '$ ' or ' $'). Defaults to "". |
currencySymbolPlacement |
Defines where the currency symbol should be placed, "p" for prefix or "s" for suffix (default). |
decimalCharacter |
Defines what decimal separator character is used. Defaults to ",". |
digitGroupSeparator |
Defines what decimal separator character is used. Defaults to ".". |
allowDecimalPadding |
Defines if decimal places should be padded with zeros. Defaults to TRUE. |
decimalPlaces |
Defines the default number of decimal places to show on the formatted value, and keep for the precision. Must be 0 or a positive integer. Defaults to 2. |
divisorWhenUnfocused |
The number that divides the element value on blur. On focus, the number is multiplied back in. Defaults to NULL. |
rawValueDivisor |
Divides the formatted value shown in the AutoNumeric
element and store the divided result in |
formatOnPageLoad |
Determine if the default value will be formatted on initialization. Defaults to TRUE. |
maximumValue |
Defines the maximum possible value a user can enter. |
minimumValue |
Defines the minimum possible value a user can enter. |
modifyValueOnWheel |
Allows the user to increment or decrement the
element value with the mouse wheel. The wheel behavior can be modified
by the |
... |
Additional parameters that can be passed to AutoNumeric. See details for more information. |
This function wraps the AutoNumeric.js library. The parameter documentation provided here should be sufficient for most users, but for those wishing to use advanced configurations it is advised to look at the documentation on the AutoNumeric GitHub repository. Alexandre Bonneau has done a wonderful job of documenting all parameters and full explanations of all parameters and their associated values can be found there.
The ...
parameter can take any of the arguments listed on the
AutoNumeric GitHub repository. A quick reference
follows:
decimalPlacesRawValue - Defines How many decimal places should be kept
for the raw value. If set to NULL (default) then decimalPlaces
is
used.
decimalPlacesShownOnBlur - Defines how many decimal places should be
visible when the element is unfocused. If NULL (default) then
decimalPlaces
is used.
decimalPlacesShownOnFocus - Defines how many decimal places should be
visible when the element has the focus. If NULL (default) then
decimalPlaces
is used.
digitalGroupSpacing - Defines how many numbers should be grouped together for the thousands separator groupings. Must be one of c("2", "2s", "3", "4"). Defaults to 3.
alwaysAllowDecimalCharacter - Defines if the decimal character or decimal character alternative should be accepted when there is already a decimal character shown in the element. If set to TRUE, any decimal character input will be accepted and will subsequently modify the decimal character position, as well as the rawValue. If set to FALSE, the decimal character and its alternative key will be dropped. This is the default setting.
createLocalList - Defines if a local list of AutoNumeric objects should be kept when initializing this object. Defaults to TRUE.
decimalCharacterAlternative - Allow to declare an alternative decimal
separator which is automatically replaced by decimalCharacter
when
typed. This is useful for countries that use a comma ',' as the decimal
character and have keyboards with numeric pads providing a period '.' as
the decimal character (in France or Spain for instance). Must be NULL
(default), ",", or ".".
emptyInputBehavior - Defines what should be displayed in the element if the raw value is missing. One of c(NULL, "focus", "press", "always", "min", "max", "zero") or a custom value. Defaults to NULL. See AutoNumeric GitHub repository for full details.
selectNumberOnly - Determine if the select all keyboard command will select the complete input text, or only the input numeric value. Defaults to TRUE.
selectOnFocus - Defines if the element value should be selected on
focus. Note: The selection is done using the selectNumberOnly
option.
Defaults to TRUE.
eventBubbles - Defines if the custom and native events triggered by AutoNumeric should bubble up or not. Defaults to TRUE.
eventIsCancelable - Defines if the custom and native events triggered by AutoNumeric should be cancelable. Defaults to TRUE.
formulaMode - Defines if the formula mode can be activated by the user. If set to true, then the user can enter the formula mode by entering the '=' character. The user will then be allowed to enter any simple math formula using numeric characters as well as the following operators: +, -, *, /, ( and ). The formula mode is exited when the user either validate their math expression using the Enter key, or when the element is blurred. Defaults to FALSE.
historySize - Set the undo/redo history table size. Defaults to 20.
isCancellable - Allow the user to cancel and undo the changes he made
to the given autonumeric-managed element, by pressing the Escape
key. Defaults to TRUE.
leadingZero - This options describes if entering 0 on the far left of the numbers is allowed, and if the superfluous zeroes should be kept when the input is blurred. One of c("allow", "deny", and "keep"). Defaults to "deny". See AutoNumeric GitHub repository for full details.
wheelOn - Defines when the wheel event will increment or decrement the element value. One of c("focus", "hover"). Defaults to "focus".
wheelStep - Defines by how much the element value should be incremented/decremented on the wheel event. Can be a set value or the string "progressive" which determines the step from the size of the input. Defaults to "progressive".
negativeBracketsTypeOnBlur - Adds brackets-like characters on negative values when unfocused. Those brackets are visible only when the field does not have the focus. The left and right symbols should be enclosed in quotes and separated by a comma. Defaults to NULL.
negativePositiveSignPlacement - Placement of the negative/positive sign
relative to the currencySymbol
option. One of c("p", "s", "l",
"r", NULL), defaults to NULL. See
AutoNumeric GitHub repository for further
documentation.
negativeSignCharacter - Defines the negative sign symbol to use. Must be a single character and be non-numeric. Defaults to "-".
positiveSignCharacter - Defines the positive sign symbol to use. Must be a single character and be non-numeric. Defaults to "+".
showPositiveSign - Allow the positive sign symbol
positiveSignCharacter
to be displayed for positive numbers.
Defaults to FALSE.
onInvalidPaste - Manage how autoNumeric react when the user tries to paste an invalid number. One of c("error", "ignore", "clamp", "truncate", "replace"). Defaults to "error".
overrideMinMaxLimits - Override the minimum and maximum limits. Must be one of c("ceiling", "floor", "ignore", NULL). Defaults to "ceiling".
readOnly - Defines if the element (<input> or another allowed html tag) should be set as read only on initialization. Defaults to FALSE.
roundingMethod - Defines the rounding method to use. One of c("S", "A", "s", "a", "B", "U", "D", "C", "F", "N05", "CHF", "U05", "D05"). Defaults to "S". See AutoNumeric GitHub repository for further documentation.
saveValueToSessionStorage - Set to TRUE to allow the
decimalPlacesShownOnFocus
value to be saved with sessionStorage.
Defaults to FALSE.
serializeSpaces - Defines how the serialize functions should treat the spaces. Either "+" (default) or "%20" (the older behavior).
showOnlyNumbersOnFocus - Defines if the element value should be converted to the raw value on focus or mouseenter, (and back to the formatted on blur or mouseleave). Defaults to FALSE.
showWarnings - Defines if warnings should be shown in the console. Defaults to TRUE.
styleRules - Defines the rules that calculate the CSS class(es) to apply on the element, based on the raw unformatted value. Defaults to NULL.
suffixText - Add a text on the right hand side of the element value. This suffix text can have any characters in its string, except numeric characters and the negative or positive sign. Defaults to NULL.
symbolWhenUnfocused - Defines the symbol placed as a suffix when not in focus or hovered. Defaults to NULL.
unformatOnHover - Defines if the element value should be unformatted when the user hover his mouse over it while holding the Alt key. Defaults to TRUE.
valuesToStrings - Provides a way for automatically replacing the formatted value with a pre-defined string, when the raw value is equal to a specific value. Defaults to NULL.
watchExternalChanges - Defines if the AutoNumeric element should watch
external changes made without using .set()
. Defaults to FALSE.
An autonumericInput object to be used in the UI function of a Shiny App.
Bonneau, Alexandre. 2018. "AutoNumeric.js javascript Package". http://autonumeric.org
Other autonumeric:
currencyInput()
,
updateAutonumericInput()
,
updateCurrencyInput()
if (interactive()) { library(shiny) library(shinyWidgets) ui <- fluidPage( h1("Autonumeric Inputs"), br(), autonumericInput( inputId = "id1", label = "Default Input", value = 1234.56 ), verbatimTextOutput("res1"), autonumericInput( inputId = "id2", label = "Custom Thousands of Dollars Input", value = 1234.56, align = "right", currencySymbol = "$", currencySymbolPlacement = "p", decimalCharacter = ".", digitGroupSeparator = ",", divisorWhenUnfocused = 1000, symbolWhenUnfocused = "K" ), verbatimTextOutput("res2"), autonumericInput( inputId = "id3", label = "Custom Millions of Euros Input with Positive Sign", value =<PHONE_NUMBER>0, align = "right", currencySymbol = "\u20ac", currencySymbolPlacement = "s", decimalCharacter = ",", digitGroupSeparator = ".", divisorWhenUnfocused = 1000000, symbolWhenUnfocused = " (millions)", showPositiveSign = TRUE ), verbatimTextOutput("res3") ) server <- function(input, output, session) { output$res1 <- renderPrint(input$id1) output$res2 <- renderPrint(input$id2) output$res3 <- renderPrint(input$id3) } shinyApp(ui, server) }


This project is a secure Bitcoin brainwallet app that will ultimately be meant for desktop and mobile platforms. It was inspired by reports of incidents of Bitcoin being seized physically at border crossings. People need an option for a brainwallet that is secure and easy to use.
NOWALLET is written in Python, it uses Electrum servers on the back end, and communicates exclusively over Tor. It uses a variant of the 'WarpWallet' technique for key derivation, rather than the typical, highly insecure method that your average brainwallet uses. Here is a basic explanation of the benefits of using the WarpWallet technique
You will be getting a secure brainwallet in a convenient desktop/mobile app, with full native and P2SH SegWit address support. You will only need to remember an email address and passphrase combination, rather than an entire 24 word mnemonic seed.
People are typically more accustomed to remembering a normal set of login info, which will protect users from forgetting or misremembering part of their seed and losing coins forever.
Finally, you will be protecting your privacy by connecting to servers over the Tor network. Optional transactional privacy features
STONEWALL and Ricochet, as well as a
Personal Server connection setting, are planned to roll out in future releases.
You can test NOWALLET, view more screenshots, and collaborate with us on Github.
Visit GithubFeel free to drop us a line on Twitter or Slack, or open an issue on Github.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/Noob.20meta.20question.html ---
What's the "correct" way to efficiently implement finitely supported functions in Lean meta. Currently I'm using list (α × β)
as α →₀ β
, but this seems totally wrong. By finitely supported, I mean functions for which I only care about the result for finitely many α
data.rbmap
?
that's the best way I know of
https://github.com/spl/lean-finmap ?
rbmap
looks about right. Thanks.
Is there a decidable
instance for mem
in rbmap
anywhere. This seems like something so fundamental that I feel like I must be doing something wrong by trying to use it.
I don't think the rbmap
theorem collection is very extensive.
I see there's contains
, which I assume is the same thing.
There doesn't seem to be an erase
either.
Is rbtree α
isomorphic to finset α
, and is rbmap α β
isomorphic to Σ s : finset α, Π x, x ∈ s → β
Last updated: Apr 24 2020 at 19:02 UTC


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Book review - Doing Data Science
Even though it sometimes feels differently, data science as a field and a profession is fairly new. Sure, sure, statistics and stuff aren’t, but the hype and unbounded optimism are recent occurrences. And that’s not a bad thing. It’s great to feel like you’re getting in on the ground floor of something that is amazing and will grow to be even more so.
Doing Data Science: Straight talk from the frontline
This book is written to give an overview of the space to those who are trying to jump into data science. Based on a class at NYU, it covers the major ideas, value, and applications of data science. It includes a review of the underlying maths, a tour of the major algorithms and methods, and a taste of important new developments like map reduce. It also has profiles of data scientists, demonstrating the different backgrounds and types of skills that can contribute to a strong career.
In addition to an introduction to the field and its ins and outs, Doing Data Science covers some of the math and programming principles of data analysis and machine learning. While this material is covered at a fairly high, not-too-technical level, it does include projects and problems for the reader to solve to engage with the subject matter. In many ways, Doing Data Science is the text version of class lectures and then the problem set for the week, minus the recitation where they actually teach you the nuts and bolts of how to do the homeworks.
What I found compelling
Data scientist profiles: We all know that data science sits at the intersection of a variety of deeper disciplines. To demonstrate this idea, and the fact that you can combine skill sets in many ways to create an exciting career, the authors profiled a series of guest lecturers who visited their class. They charted their skills in:
- Visualization
- Computer science
- Math
- Stats
- Machine learning
- Domain expertise
- Presentation and communication
- Data
By profiling a series of data scientists, it shows that you can be great in some areas and just okay in others and still have a successful career. It’s all about how you combine your skills and apply them to solving important problems.
High-level introduction: I liked the high level introduction to the most important methods in data science. So many introductory works belabor the math, forgetting that learning is an iterative process. Don’t get me wrong: I think it’s very important to learn the underlying theory and math, but it can be a lot to digest when you’re just trying to get into a topic. O’Neil and Schutt do a good job of giving the appropriate rigor while keeping it understandable and introductory.
Non-method materials: There’s a lot more to data science than just the algorithms. In fact, you could argue that they are a core but minor part of being successful. The authors do a great job of going beyond the algorithms and methods, engaging in topics like where you get data, visualizing and presenting results, participating in competitions, and fitting data science into business. These are important ideas that usually don’t get enough air time.
What I didn’t love
Since this is a high-level, introductory work, I felt like the very involved projects at the end of each chapter were a little unnecessary. In fact, they were kind of distracting. I understand that they were a critical part of the course, but without the supporting academic structures, they felt a bit like an afterthought. I’m using other books to learn more about the details of the methods. I’ll use those resources as I practice applying what I learn. This book would be better off presenting the material, which is terrific, and leaving it at that.


| DOSE: T+ 0:00 |
3.5 g |
oral |
Mushrooms |
(dried) |
| T+ 2:25 |
1 cig. |
smoked |
Cannabis |
(plant material) |
| T+ 2:50 |
3 bowls |
smoked |
Cannabis |
(plant material) |
Great mushrooms first semester freshman year of college. They were large and obviously well grown, with streaks of blue, and the top of the cap was bumpy, only slightly convex and was a light brown which got darker towards the top. About ĺ up on some of the bigger stems was what I think is called a mycelin ring. My friend pointed out what he called an ďelephant foot formationĒ at the bottom of all the stems, because they did look really different and resembled a paw or foot or something. The smaller ones were closed caps. That probably doesnít help for ID purposes, but he said he thinks they were cubensis. He boasted that these were his prize shrooms, and told me that he never had anything so potent, and warned me to be careful with them. I got a half ounce. I got pulled over on the way down, wreckless driving on highway 74, and saw my life flash before my eyes which may have contributed to bad vibes that would come later.
While driving I kept thinking about how good the shrooms looked and called my friend who weíll call S who had been wanting to try it for a long time, and said he would when I got back. When I arrived on campus, I met up with S and we went off to the cafeteria to eat the shwaggity food. I just made myself a small salad. I had done shrooms 4 times before this, smoked daily, and had done dxm and diphenhydramine countless times and thought I could handle the shrooms with an almost empty stomach. I never got visuals on shrooms even though my friends had after eating the same amount. They would get visuals and still be functioning more normally than I would be.  I do an 8th, S does a 1/16. Worst tasting shrooms Iíve ever eaten. S thinks they taste good and I think heís lying. Iím feeling a little bit on edge, probably from the events that had happened before I got into town. Iím tired because road trips always take a lot out of me, and I only got 5 hours of sleep the previous night. I write a brief note of my intentions for this trip.  Really I just wanted to have full on visuals. I write down the time with a sharpie on my right hand for future reference. I knew that I wasnít in the best mindset for tripping, but thought Iíd be okay because of my experience and the fact that I was so in the mood.
6:15 Ė Iím starting to feel nauseous, uneasy, and uncomfortable. I canít really think. S seems confused because heís never even done it before and he doesnít feel anything so he keeps asking me tons and tons of questions which is starting to annoy me.
6:30 Ė I feel either too hot or too cold, and have the chills. I continuously put on and take off my jacket. I have trouble catching my breath. I am starting to worry a little bit, and Sís continuous questions about how Iím feeling are not helping the situation at all. My stomach is churning, and I feel weak. I lie down on Sís bed, and his roommate looks at us strangely because he doesnít do drugs and thinks that weíre destroying our brains, which adds to my anxiety.
6:40 Ė we go outside for a change of environment. Bad idea. All S wants to do is talk with people, and is very open about the fact that heís on shrooms, telling random people. Iím not comfortable with this at all. I begin to suspect that people are making all kinds of judgements about my drug wisdom, my mental capacity, my sanity, my benevolence. I feel very on edge, and almost snappy. I most certainly donít want to be talking with these people, as I am a more natural introvert, especially on shrooms.
6:45 Ė I see some friends and they look concerned because I am visibly distressed. My friend Z is especially concerned and is tending to my needs and gets me water and tells me everythingís gonna be cool. I find myself extremely attracted to her, I feel like Iím in love with her.
6:55 Ė Z has left, MK stops by. He had offered earlier to drive us around to smoke a blunt, and was seeing how we were doing. A few people are hanging out in my room, and some chill music gets put on. I lie down in my bed and tell everyone Iím going to go to sleep, because I feel tired. Itís only been an hour and Iím already tripping balls, and Iím scared. Iím starting to get slight trails off things, And my posters all have an evil look to them, even my bob Marley poster.  I gag hard, but I donít vomit. I look down at my hands and thereís blood on one of my fingers. I start freaking out. Maybe I scratched and cut my throat with my sharp tooth bitten finger nails. I look in the mirror and theres a miniscule bit of blood smeared down from my lip. Somebody else also sees it so Iím not just hallucinating.
7:10 - Iím pretty sure Iím dying now. My suitemate, M, comes in because he hears that Iím not having a good time. Heís talking to other people in the room, who heís not really friends with and mentioned once in a subtle way that he thought they were sketchy. Iím getting more and more scared, as I lie in bed under the sheets, feeling like a mental patient. I think I canít control my emotions, and I mention Zís hot and ďI like her style.Ē At the same time Iím thinking why did I do this to myself. Maybe Iím never going to get my brain back. Iíll never be able to write a novel (thatís my goal).   But also he keeps saying how awesome everything is which is making me feel worse like I got cheated, and he keeps trying to compare his trip to mine. I ask M to please call X (they donít know each other at all) because Iím afraid I canít converse with sober people, and I want to make sure the shrooms arenít poisonous, which of course they are not. I feel dumb about this, and this only made me more embarrassed. I keep saying that I feel like Iím going to die, and M keeps saying you donít want to die and youíre not going to. I took this as he thought I was suicidal, which made me more paranoid and anxious because I didnít want him to think I was faking all of this for attention.
7:20 Ė The auditory effects are really kicking in, everything kind of drones and echoís, and it feels like my heads under the water in a bathtub, feeling all of the vibrations and sounds. Music sounds otherworldly, it was Piper At the Gates of Dawn by Pink Floyd. I notice how all the sound effects, different time signatures, and all those intricate little details they put into their music are made for people like me. Its not instant gratification, its something you have to sit down and listen to carefully to appreciate the entire work. Iím just blown away at how good they are at tripping me out. Then Iím afraid maybe Iíll end up like Syd Barrett, crazy, living with my mom, the sad byproduct of artistic genius, and then at some point Iíll die a lonely person.  I convince myself that maybe this is why Iím having a bad trip, because Iím holding everything back. Iím holding back from all social situations, and using weed and my strange musical and movie obsessions as a crutch to avoid real bonding between people. I always feel like a chameleon in a group of people. Iím always afraid to get too close because Iím afraid that if they really get to know me, theyíll see me for the scared and insecure little boy I am. I donít want people to think Iím clingy,desperate, a loser, or an emotional nutcase. If they see all of this, they wonít accept me. Thatís it, I have attachment issues! Wow. I wonder if Iíll still feel the same way about Z the next morning.
7:50 Ė M has to go but asks if I would sell him some mushrooms. He never really said anything special, but his words were so calm it really grounded me, and I really appreciate that. I eyed out a generous 1/16 making sure that it was more rather than less. I later found out it was actually about an 8th, but never brought it up. Not just because I was grateful, but because he would be more understanding of my momentary trainwreck of a trip if he ate what he thought was a 1/16 and tripped twice as hard. I felt extremely intelligent with my reasoning, sneaky and kind at the same time. I tell my friends Iím going to be fine, and that I just want to chill for a second and to leave me alone.
8:00 Ė After hiding under my covers for a little while, and focusing on the sheets which have a life of their own, I am suddenly no longer nauseous at all. I am hesitant to get up and leave the security of my bed, much less the possibility of leaving my room, or the dorms. But then I realize that there is nothing to be afraid and all of a sudden I feel fine, not great, like S and his shit eating grin, but I feel more clearheaded. Midnite Vultures by Beck is now on and Iím feeling the groove. I stand up, jump off the bed and say with a forced smile, ďLetís go smoke that Blunt.Ē
8:10 Ė As MK is giving us a ride to Jís flat in downtown Wilmington, my thoughts are flying at 100 thoughts a second with epiphanies. Iím glad I got out of that freaky funk.
8:25 Ė J tells me about a few bad trips heís had, and I can relate to some of it from what I had just experienced. I had a funny taste in my mouth the whole time, I think it was the taste of shrooms. Whenever I burped I could taste them and it sent shudders down my spine and made my slightly nauseous again. I have cotton mouth which is weird because Iíve been drinking tons of water this whole time to try to get the shrooms out of my system. I get another glass for the blunt session, and then spark up, Iím peaking now so itís perfect timing for the blunt. Itís a tight, evenly rolled bob Marley cone of a blunt, pretty good weed, great weed for Wilmington, and S and I eventually both have to pass on it.
8:50 Ė Then they load up Jís massive reverse shotgun for some reason, as if we arenít high. I guess we forgot that we had just passed on the blunt. S is in another world and saying one second how weíre midgets and the next that weíre ogres.  We try to calm him down, but he seems like heís having a good time jerking back and forth. He goes from seeming scared one second and saying ďNO! oh shit!Ē to laughing hysterically seconds later.  Are you insane?!!  This was funny, but I tried to get him to stop and he did eventually.  I look at the ceiling and can see balls of color swirling around. I think I can feel my brain sizzling. The weed dulled the stream of consciousness. But there are a few thoughts still cycling. One thing I kept pondering was my attachment issues, the implications of my introversion, and my main goal in life, to write a best selling novel.  But I do. Is it to pass my name on for the same reason others would have a child, is it to change the social subconscious.
 The irony, what Iíve been against forever, not selling out and not being like my old man. Iím marketing a book to a demograph of rebellious teenagers, and it will sell when I finish it because I know what they want to hear and I know what theyíre interested in. Whatís more is Iíll be making fun of them and their emotions and their idealistic views on life with subtlety, and only my most perceptive and intelligent fans will see that. It will be a kick to the balls for all of the pseudo intellectuals I have come across in my years, and those to come.
The song those to come by The Shins had been stuck in my head that whole week and during the entire blunt session, and I had been contemplating the meaning, and it matched up perfectly with my views of life, what I was thinking at the time, and my artistic goals and it put an existential twist on it.
10:00 Ė From here we watch the Salival Dvd at full volume. I am mesmerized. S says ďscreamingĒ music is a buzzkill, but then decides itís awesome. Weíre all zoned out.
11:30 Ė the effects have been gradually wearing off. I still feel something, but the crazy thoughts are gone. Everything still feels symbolic, but Iím more stable emotionally. Residual tripping effects last until about 1:00 until I try to go to sleep because Iím exhausted. I didnít get to sleep until 3:30 or so.
The next day I was drained. I felt withdrawn, which was disappointing because I couldnít let myself open up like I promised myself Iíd do somewhere in the trip. I felt disconnected. I didnít feel the same way about Z, which was weird because I felt it so strongly the night before.
 I wouldnít call it life changing because most of the things I ďlearnedĒ I wouldíve learned in a short manner of time anyway, or I already knew them. I donít do drugs anymore, not even alcohol. However I learned after this that itís dumb for me to trip when I havenít gotten much sleep. When I donít eat much it makes the effects more intense, but I get extreme nausea which puts me on edge and can ruin the trip at points. So always eat a good meal 2 or 3 hours before tripping.  The blood in the bathroom probably came from a popped zit.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


In the project I am working on, I have to write a test for a function that download CSV from external website and store it locally. I am pretty new in Go so please let me know if I am doing it wrong.
The function is pretty simple, it uses the Go net/http
package to send a GET
request and os
package to write the HTTP response to a local file. See the full code below:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
|
The function we want to test is DownloadCSV
which expects two arguments. url
is a URL endpoint that host the CSV and filename
is the name of local file to store the HTTP response.
There are three things I want to test here:
http.Get
shouldn’t return an error- It should create a local CSV file
- The content of the local CSV file should match with the server
http.Get
shouldn’t return an error
To test the first one, we will use an awesome Go package called net/http/httptest
. This package allows us to create a HTTP server and set the response to whatever you want.
In the code below, you can see we start with creating the server which return a 200 response status ( line 10 – 12 ). After that, we pass the URL of the server to the DownloadCSV
function and also the required filename.
At the end, we do the assertion to make sure the function does not return an error. We are using Go testing package Errorf
method to output the message if the assertion fail. This is important because we need it to mark the test as FAIL
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
|
It should create a local CSV file
We are going to use the os
package to check the file existence and delete it after the test finished. Three functions we will use are Stat
, IsNotExist
and Remove
. Stat
and IsNotExists
are used to assert the file existence and Remove
will clean up the file after test finished.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 |
|
The content of the local CSV file should match with the server
The simplest way to do this test is to configure the httptest
server to return a string and check if that string exists in the test file. We can take this a bit further by using an actual CSV file.
We can use test fixture to achieve this. We will create a new folder called testdata
, put a CSV file inside it, set the httptest
server to read the file using ioutil.ReadFile
and return the content to the client. We will also use ioutil.ReadFile
to do the assertion to compare the content of the files. Here is the final version of the test:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 |
|
You can check out the full code here: https://github.com/rudylee/go-playground and please leave a comment if find any errors.


White to play 56
pips White=162 Black=188
(CorrectAnswer= 0/0, ErrorScore= 0)
1. Bar/20 21/15 eq: -0.195 44.63% (G: 8.70% B:0.32%) 55.37% (G:11.36% B:0.35%) 2. Bar/14 eq: -0.203 (-0.007) 44.70% (G: 9.29% B:0.34%) 55.30% (G:12.69% B:0.41%) 3. Bar/20 13/7 eq: -0.222 (-0.026) 44.15% (G: 9.51% B:0.37%) 55.85% (G:13.33% B:0.42%) 4. Bar/20 8/2 eq: -0.273 (-0.078) 42.00% (G: 8.76% B:0.31%) 58.00% (G:12.35% B:0.42%)


Input class for Pin D6.
#include <driver/gpio/stm32/gpio.hpp>
|
|
static constexpr Port | port |
| | Port name.
|
| |
|
static constexpr uint8_t | pin |
| | Pin Number.
|
| |
|
static constexpr uint16_t | mask |
| | Bitmask for registers that contain a 1bit value for every pin.
|
| |
|
static constexpr uint32_t | mask2 |
| | Bitmask for registers that contain a 2bit value for every pin.
|
| |
|
static constexpr Direction | direction |
| | Declares the Pin direction.
|
| |
| static constexpr bool | Low |
| | These constants refer to the logical state of the GPIO. More...
|
| |
| static constexpr bool | High |
| | These constants refer to the logical state of the GPIO. More...
|
| |
|
| enum | InputType {
InputType::Floating,
InputType::PullUp,
InputType::PullDown
} |
| |
| enum | OutputType {
OutputType::PushPull,
OutputType::OpenDrain
} |
| |
| enum | OutputSpeed {
OutputSpeed::MHz2,
OutputSpeed::MHz25,
OutputSpeed::MHz50,
OutputSpeed::MHz100
} |
| |
| enum | InputTrigger {
RisingEdge,
FallingEdge,
BothEdges
} |
| |
| enum | Port {
A,
C,
B,
E,
D,
H
} |
| | The Port a Gpio Pin is connected to.
|
| |
| enum | Direction {
In,
Out,
InOut,
Special
} |
| | Declares the direction of a GPIO.
|
| |
| enum | Mode {
Input,
Output,
AlternateFunction,
Analog,
Mask
} |
| | I/O Direction Mode values for this specific pin.
|
| |
| enum | AlternateFunction {
AlternateFunction::AF_0,
AlternateFunction::AF_1,
AlternateFunction::AF_2,
AlternateFunction::AF_3,
AlternateFunction::AF_4,
AlternateFunction::AF_5,
AlternateFunction::AF_6,
AlternateFunction::AF_7,
AlternateFunction::AF_8,
AlternateFunction::AF_9,
AlternateFunction::AF_10,
AlternateFunction::AF_11,
AlternateFunction::AF_12,
AlternateFunction::AF_13,
AlternateFunction::AF_14,
AlternateFunction::AF_15
} |
| |
◆ acknowledgeExternalInterruptFlag()
| static void xpcc::stm32::GpioInputD6::acknowledgeExternalInterruptFlag |
( |
| ) |
|
|
inlinestatic |
The documentation for this struct was generated from the following file:
- build/stm32f4_discovery/blink/libxpcc/generated_platform/driver/gpio/stm32/gpio.hpp


This project realizes an exercise submission and replay mechanism for arbitrary
exercise submission servers. As a default implementation, submission to a structured
SVN repository is implemented (requires task/username) structures in the
repository. Readable directories on second level are available for replay (as corrected
submissions), read- and writable directories on second level are available for submission.
The main configuration of the program can be stored in a system directory (to be unmodifiable
for users) or in the main directory of the installation or jar file. For further details on the
configuration c.f. de.uni_hildesheim.sse.exerciseSubmitter.configuration.IConfiguration
and the documentation of the individual plugins.
The following lines show an example configuration (in c:\windows\exerciseSubmission.ini
,
/etc/exerciseSubmission.ini
or the main directory of the bundling jar file where
the classes of this program are packed in):
debug = true
protocol.1 = svn
svn.server = https://<IP_ADDRESS>/svn/test/
review = file
review.storeInExercise = true
review.resubmitExercise = true
Short description: debugging is enabled, one submission server using the SVN protocol is specified,
reviews will be stored in file (using the file review protocol). When a review is done, the results
will be stored in the exercise (ready for resubmission). Thereby, the reviewed exercise annotated
by // REVIEW
comments will automatically be resubmitted to the (all) submission server(s).
Individual submission/review protocols may introduce further options.


Mdt 2013 Installing Mdt 2013
Now we are going to install MDT 2013. This is a simple process but I wanted to show you anyways.
After you have downloaded MDT 2013 from Microsoft (See This Post)
When you first Launch the MDT 2013 Installer, you should see the following screen:
Click Next to Continue
Accept the License Agreement and click Next:
Choose the Location that you want MDT 2013 to be installed. This is typically left alone but you are more than welcome to install it on a separate partition/drive. Once you’ve chosen the appropriate location, Click Next:
I typically choose not to join their CEIP but it’s up to you. Click Next:
Now, Click Install
That’s It! It’s pretty straight forward…. Now onto the good stuff! We will be setting up our MDT 2013 Deployment Share in the next post.
Also, I am in the process of making a video that will explain and show all of these steps
Enjoy!


| A1A2A3 |
The A1A2A3 Blood Group System |
| AA.Aa.aa |
The AA-Aa-aa Blood Group System |
| AB.Ab.aB.ab |
The AB-Ab-aB-ab Blood Group System |
| ABO |
The ABO Blood Group System |
| acat |
Ordinal Regression with Adjacent Categories Probabilities |
| add1.vglm |
Add or Drop All Possible Single Terms to/from a Model |
| AICc-method |
Akaike's Information Criterion |
| AICqrrvglm |
Akaike's Information Criterion |
| AICrrvgam |
Akaike's Information Criterion |
| AICrrvglm |
Akaike's Information Criterion |
| AICvgam |
Akaike's Information Criterion |
| AICvlm |
Akaike's Information Criterion |
| alaplace1 |
Asymmetric Laplace Distribution Family Functions |
| alaplace2 |
Asymmetric Laplace Distribution Family Functions |
| alaplace3 |
Asymmetric Laplace Distribution Family Functions |
| alclevels |
Crashes on New Zealand Roads in 2009 |
| alcoff |
Crashes on New Zealand Roads in 2009 |
| altered |
Altered, Inflated and Truncated Values in GAIT Regression |
| amlbinomial |
Binomial Logistic Regression by Asymmetric Maximum Likelihood Estimation |
| amlexponential |
Exponential Regression by Asymmetric Maximum Likelihood Estimation |
| amlnormal |
Asymmetric Least Squares Quantile Regression |
| amlpoisson |
Poisson Regression by Asymmetric Maximum Likelihood Estimation |
| anova.vglm |
Analysis of Deviance for Vector Generalized Linear Model Fits |
| AR1 |
Autoregressive Process with Order-1 Family Function |
| AR1EIM |
Computation of the Exact EIM of an Order-1 Autoregressive Process |
| as.character.SurvS4 |
Create a Survival Object |
| as.data.frame.SurvS4 |
Create a Survival Object |
| auuc |
Auckland University Undergraduate Counts Data |
| aux.posbernoulli.t |
Auxiliary Function for the Positive Bernoulli Family Function with Time Effects |
| backPain |
Data on Back Pain Prognosis, from Anderson (1984) |
| backPain2 |
Data on Back Pain Prognosis, from Anderson (1984) |
| beggs |
Bacon and Eggs Data |
| bell |
The Bell Series of Integers |
| Benford |
Benford's Distribution |
| Benini |
The Benini Distribution |
| benini1 |
Benini Distribution Family Function |
| Betabinom |
The Beta-Binomial Distribution |
| betabinomial |
Beta-binomial Distribution Family Function |
| betabinomialff |
Beta-binomial Distribution Family Function |
| betaff |
The Two-parameter Beta Distribution Family Function |
| Betageom |
The Beta-Geometric Distribution |
| betageometric |
Beta-geometric Distribution Family Function |
| betaII |
Beta Distribution of the Second Kind |
| Betanorm |
The Beta-Normal Distribution |
| betaprime |
The Beta-Prime Distribution |
| betaR |
The Two-parameter Beta Distribution Family Function |
| Biamhcop |
Ali-Mikhail-Haq Bivariate Distribution |
| biamhcop |
Ali-Mikhail-Haq Distribution Family Function |
| biclaytoncop |
Clayton Copula (Bivariate) Family Function |
| BICvgam |
Bayesian Information Criterion |
| BICvlm |
Bayesian Information Criterion |
| Bifgmcop |
Farlie-Gumbel-Morgenstern's Bivariate Distribution |
| bifgmcop |
Farlie-Gumbel-Morgenstern's Bivariate Distribution Family Function |
| bifgmexp |
Bivariate Farlie-Gumbel-Morgenstern Exponential Distribution Family Function |
| bifrankcop |
Frank's Bivariate Distribution Family Function |
| bigamma.mckay |
Bivariate Gamma: McKay's Distribution |
| bigumbelIexp |
Gumbel's Type I Bivariate Distribution Family Function |
| bilogis |
Bivariate Logistic Distribution |
| bilogistic |
Bivariate Logistic Distribution Family Function |
| Binom2.or |
Bivariate Odds Ratio Model |
| binom2.or |
Bivariate Binary Regression with an Odds Ratio (Family Function) |
| Binom2.rho |
Bivariate Probit Model |
| binom2.Rho |
Bivariate Probit Regression |
| binom2.rho |
Bivariate Probit Regression |
| binomialff |
Binomial Family Function |
| Binorm |
Bivariate Normal Distribution Cumulative Distribution Function |
| binormal |
Bivariate Normal Distribution Family Function |
| binormalcop |
Gaussian Copula (Bivariate) Family Function |
| Binormcop |
Gaussian Copula (Bivariate) Distribution |
| Biplackett |
Plackett's Bivariate Copula |
| biplackettcop |
Plackett's Bivariate Copula Family Function |
| biplot-method |
Biplot of Constrained Regression Models |
| biplot.rrvglm |
Latent Variable Plot for RR-VGLMs |
| Bisa |
The Birnbaum-Saunders Distribution |
| bisa |
Birnbaum-Saunders Regression Family Function |
| Bistudentt |
Bivariate Student-t Distribution Cumulative Distribution Function |
| bistudentt |
Bivariate Student-t Family Function |
| bmi.nz |
Body Mass Index of New Zealand Adults Data |
| borel.tanner |
Borel-Tanner Distribution Family Function |
| Bort |
The Borel-Tanner Distribution |
| Brat |
Inputting Data to fit a Bradley Terry Model |
| brat |
Bradley Terry Model |
| bratt |
Bradley Terry Model With Ties |
| Dagum |
The Dagum Distribution |
| dagum |
Dagum Distribution Family Function |
| dalap |
The Laplace Distribution |
| dAR1 |
The AR-1 Autoregressive Process |
| dbenf |
Benford's Distribution |
| dbenini |
The Benini Distribution |
| dbetabinom |
The Beta-Binomial Distribution |
| dbetabinom.ab |
The Beta-Binomial Distribution |
| dbetageom |
The Beta-Geometric Distribution |
| dbetanorm |
The Beta-Normal Distribution |
| dbiamhcop |
Ali-Mikhail-Haq Bivariate Distribution |
| dbiclaytoncop |
Clayton Copula (Bivariate) Distribution |
| dbifgmcop |
Farlie-Gumbel-Morgenstern's Bivariate Distribution |
| dbifrankcop |
Frank's Bivariate Distribution |
| dbilogis |
Bivariate Logistic Distribution |
| dbinom2.or |
Bivariate Odds Ratio Model |
| dbinom2.rho |
Bivariate Probit Model |
| dbinorm |
Bivariate Normal Distribution Cumulative Distribution Function |
| dbinormcop |
Gaussian Copula (Bivariate) Distribution |
| dbiplackcop |
Plackett's Bivariate Copula |
| dbisa |
The Birnbaum-Saunders Distribution |
| dbistudentt |
Bivariate Student-t Distribution Cumulative Distribution Function |
| dbort |
The Borel-Tanner Distribution |
| dcard |
Cardioid Distribution |
| ddagum |
The Dagum Distribution |
| ddiffzeta |
Differenced Zeta Distribution |
| deermice |
Captures of Peromyscus maniculatus (Also Known as Deer Mice). |
| deexp |
Expectiles of the Exponential Distribution |
| denorm |
Expectiles of the Normal Distribution |
| deplot.lmscreg |
Density Plot for LMS Quantile Regression |
| depvar |
Response Variable Extracted |
| deunif |
Expectiles of the Uniform Distribution |
| dexpgeom |
The Exponential Geometric Distribution |
| dexplog |
The Exponential Logarithmic Distribution |
| dexppois |
The Exponential Poisson Distribution |
| dextlogF |
Extended log-F Distribution |
| df.residual |
Residual Degrees-of-Freedom |
| df.residual_vlm |
Residual Degrees-of-Freedom |
| dfbeta |
Hat Values and Regression Deletion Diagnostics |
| dfbetavlm |
Hat Values and Regression Deletion Diagnostics |
| dfelix |
The Felix Distribution |
| dfisk |
The Fisk Distribution |
| dfoldnorm |
The Folded-Normal Distribution |
| dfrechet |
The Frechet Distribution |
| dgaitbinom |
Generally-Altered, -Inflated and -Truncated Binomial Distribution |
| dgaitlog |
Generally-Altered, -Inflated and -Truncated Logarithmic Distribution |
| dgaitnbinom |
Generally-Altered, -Inflated and -Truncated Negative Binomial Distribution |
| dgaitplot |
Plotting the GAIT Combo Density |
| dgaitpois |
Generally-Altered, -Inflated and -Truncated Poisson Distribution |
| dgaitzeta |
Generally-Altered, -Inflated and -Truncated Zeta Distribution |
| dgenbetaII |
The Generalized Beta II Distribution |
| dgengamma.stacy |
Generalized Gamma Distribution |
| dgenpois0 |
Generalized Poisson Distribution (Original Parameterization) |
| dgenpois1 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| dgenpois2 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| dgenray |
The Generalized Rayleigh Distribution |
| dgev |
The Generalized Extreme Value Distribution |
| dgompertz |
Gompertz Distribution |
| dgpd |
The Generalized Pareto Distribution |
| dgumbel |
The Gumbel Distribution |
| dgumbelII |
The Gumbel-II Distribution |
| dhuber |
Huber's Least Favourable Distribution |
| dhzeta |
Haight's Zeta Distribution |
| Diffzeta |
Differenced Zeta Distribution |
| diffzeta |
Differenced Zeta Distribution Family Function |
| dimm |
Utility Functions for the VGAM Package |
| dinv.gaussian |
The Inverse Gaussian Distribution |
| dinv.lomax |
The Inverse Lomax Distribution |
| dinv.paralogistic |
The Inverse Paralogistic Distribution |
| dirichlet |
Fitting a Dirichlet Distribution |
| dirmul.old |
Fitting a Dirichlet-Multinomial Distribution |
| dirmultinomial |
Fitting a Dirichlet-Multinomial Distribution |
| dkumar |
The Kumaraswamy Distribution |
| dlaplace |
The Laplace Distribution |
| dlgamma |
The Log-Gamma Distribution |
| dlind |
The Lindley Distribution |
| dlino |
The Generalized Beta Distribution (Libby and Novick, 1982) |
| dlog |
Logarithmic Distribution |
| dlogF |
log F Distribution |
| dloglap |
The Log-Laplace Distribution |
| dlomax |
The Lomax Distribution |
| dmakeham |
The Makeham Distribution |
| dmaxwell |
The Maxwell Distribution |
| dnaka |
Nakagami Distribution |
| double.cens.normal |
Univariate Normal Distribution with Double Censoring |
| double.expbinomial |
Double Exponential Binomial Distribution Family Function |
| dparalogistic |
The Paralogistic Distribution |
| dpareto |
The Pareto Distribution |
| dparetoI |
The Pareto(IV/III/II) Distributions |
| dparetoII |
The Pareto(IV/III/II) Distributions |
| dparetoIII |
The Pareto(IV/III/II) Distributions |
| dparetoIV |
The Pareto(IV/III/II) Distributions |
| dperks |
The Perks Distribution |
| dpois.points |
Poisson Points Distribution |
| dpolono |
The Poisson Lognormal Distribution |
| dposbern |
Positive Bernoulli Sequence Model |
| dposgeom |
Positive-Geometric Distribution |
| dposnorm |
The Positive-Normal Distribution |
| drayleigh |
Rayleigh Distribution |
| drice |
The Rice Distribution |
| drop1.vglm |
Add or Drop All Possible Single Terms to/from a Model |
| dsc.t2 |
Expectiles/Quantiles of the Scaled Student t Distribution with 2 Df |
| dsimplex |
Simplex Distribution |
| dsinmad |
The Singh-Maddala Distribution |
| dskellam |
The Skellam Distribution |
| dskewnorm |
Skew-Normal Distribution |
| dslash |
Slash Distribution |
| dtobit |
The Tobit Distribution |
| dtopple |
The Topp-Leone Distribution |
| dtriangle |
The Triangle Distribution |
| dtrinorm |
Trivariate Normal Distribution Density and Random Variates |
| dtruncpareto |
The Truncated Pareto Distribution |
| ducklings |
Relative Frequencies of Serum Proteins in White Pekin Ducklings |
| dyules |
Yule-Simon Distribution |
| dzabinom |
Zero-Altered Binomial Distribution |
| dzageom |
Zero-Altered Geometric Distribution |
| dzanegbin |
Zero-Altered Negative Binomial Distribution |
| dzapois |
Zero-Altered Poisson Distribution |
| dzeta |
The Zeta Distribution |
| dzibinom |
Zero-Inflated Binomial Distribution |
| dzigeom |
Zero-Inflated Geometric Distribution |
| dzinegbin |
Zero-Inflated Negative Binomial Distribution |
| dzipf |
The Zipf Distribution |
| dzipfmb |
The Zipf-Mandelbrot Distribution |
| dzipois |
Zero-Inflated Poisson Distribution |
| dzoabeta |
The Zero/One-Inflated Beta Distribution |
| dzoibetabinom |
The Beta-Binomial Distribution |
| dzoibetabinom.ab |
The Beta-Binomial Distribution |
| eCDF |
Empirical Cumulative Distribution Function |
| eCDF.vglm |
Empirical Cumulative Distribution Function |
| edhuber |
Huber's Least Favourable Distribution |
| eexp |
Expectiles of the Exponential Distribution |
| enorm |
Expectiles of the Normal Distribution |
| enzyme |
Enzyme Data |
| erf |
Error Function, and variants |
| erfc |
Error Function, and variants |
| erlang |
Erlang Distribution |
| eunif |
Expectiles of the Uniform Distribution |
| Expectiles-Exponential |
Expectiles of the Exponential Distribution |
| Expectiles-Normal |
Expectiles of the Normal Distribution |
| Expectiles-sc.t2 |
Expectiles/Quantiles of the Scaled Student t Distribution with 2 Df |
| Expectiles-Uniform |
Expectiles of the Uniform Distribution |
| expexpff |
Exponentiated Exponential Distribution |
| expexpff1 |
Exponentiated Exponential Distribution |
| expexpint |
The Exponential Integral and Variants |
| expgeom |
The Exponential Geometric Distribution |
| expgeometric |
Exponential Geometric Distribution Family Function |
| expint |
The Exponential Integral and Variants |
| expint.E1 |
The Exponential Integral and Variants |
| explink |
Exponential Link Function |
| explog |
The Exponential Logarithmic Distribution |
| explogff |
Exponential Logarithmic Distribution Family Function |
| exponential |
Exponential Distribution |
| exppois |
The Exponential Poisson Distribution |
| exppoisson |
Exponential Poisson Distribution Family Function |
| extlogF1 |
Extended log-F Distribution Family Function |
| extlogitlink |
Logit Link Function |
| Gaitbinom |
Generally-Altered, -Inflated and -Truncated Binomial Distribution |
| Gaitlog |
Generally-Altered, -Inflated and -Truncated Logarithmic Distribution |
| gaitlog |
Generally-Altered, -Inflated and -Truncated Logarithmic Regression |
| Gaitnbinom |
Generally-Altered, -Inflated and -Truncated Negative Binomial Distribution |
| Gaitpois |
Generally-Altered, -Inflated and -Truncated Poisson Distribution |
| gaitpoisson |
Generally-Altered, -Inflated and -Truncated Poisson Regression |
| Gaitzeta |
Generally-Altered, -Inflated and -Truncated Zeta Distribution |
| gaitzeta |
Generally-Altered, -Inflated and -Truncated Zeta Regression |
| gamma1 |
1-parameter Gamma Regression Family Function |
| gamma2 |
2-parameter Gamma Regression Family Function |
| gammahyperbola |
Gamma Hyperbola Bivariate Distribution |
| gammaR |
2-parameter Gamma Regression Family Function |
| garma |
GARMA (Generalized Autoregressive Moving-Average) Models |
| gaussianff |
Univariate Normal Distribution |
| GenbetaII |
The Generalized Beta II Distribution |
| genbetaII |
Generalized Beta Distribution of the Second Kind |
| gengamma.stacy |
Generalized Gamma distribution family function |
| gengammaUC |
Generalized Gamma Distribution |
| Genpois0 |
Generalized Poisson Distribution (Original Parameterization) |
| Genpois1 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| Genpois2 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| genpoisson0 |
Generalized Poisson Regression (Original Parameterization) |
| genpoisson1 |
Generalized Poisson Regression (GP-1 Parameterization) |
| genpoisson2 |
Generalized Poisson Regression (GP-2 Parameterization) |
| genray |
The Generalized Rayleigh Distribution |
| genrayleigh |
Generalized Rayleigh Distribution Family Function |
| geometric |
Geometric (Truncated and Untruncated) Distributions |
| get.smart |
Retrieve One Component of ".smart.prediction" |
| get.smart.prediction |
Retrieves ".smart.prediction" |
| gev |
Generalized Extreme Value Regression Family Function |
| gevff |
Generalized Extreme Value Regression Family Function |
| gevUC |
The Generalized Extreme Value Distribution |
| gew |
General Electric and Westinghouse Data |
| Gompertz |
Gompertz Distribution |
| gompertz |
Gompertz Regression Family Function |
| gordlink |
Gamma-Ordinal Link Function |
| gpd |
Generalized Pareto Distribution Regression Family Function |
| gpdUC |
The Generalized Pareto Distribution |
| grain.us |
Grain Prices Data in USA |
| grc |
Row-Column Interaction Models including Goodman's RC Association Model and Unconstrained Quadratic Ordination |
| gumbel |
Gumbel Regression Family Function |
| Gumbel-II |
The Gumbel-II Distribution |
| gumbelff |
Gumbel Regression Family Function |
| gumbelII |
Gumbel-II Regression Family Function |
| guplot |
Gumbel Plot |
| guplot.default |
Gumbel Plot |
| guplot.vlm |
Gumbel Plot |
| lakeO |
Annual catches on Lake Otamangakau from October 1974 to October 1989 |
| lambertW |
The Lambert W Function |
| laplace |
Laplace Regression Family Function |
| latvar |
Latent Variables |
| leipnik |
Leipnik Regression Family Function |
| lerch |
Lerch Phi Function |
| leukemia |
Acute Myelogenous Leukemia Survival Data |
| levy |
Levy Distribution Family Function |
| lgamma1 |
Log-gamma Distribution Family Function |
| lgamma3 |
Log-gamma Distribution Family Function |
| lgammaUC |
The Log-Gamma Distribution |
| Lindley |
The Lindley Distribution |
| lindley |
1-parameter Gamma Distribution |
| linkfun |
Link Functions |
| linkfun.vglm |
Link Functions for VGLMs |
| Links |
Link functions for VGLM/VGAM/etc. families |
| Lino |
The Generalized Beta Distribution (Libby and Novick, 1982) |
| lino |
Generalized Beta Distribution Family Function |
| lirat |
Low-iron Rat Teratology Data |
| lms.bcg |
LMS Quantile Regression with a Box-Cox transformation to a Gamma Distribution |
| lms.bcn |
LMS Quantile Regression with a Box-Cox Transformation to Normality |
| lms.yjn |
LMS Quantile Regression with a Yeo-Johnson Transformation to Normality |
| lms.yjn2 |
LMS Quantile Regression with a Yeo-Johnson Transformation to Normality |
| Log |
Logarithmic Distribution |
| log1mexp |
Logarithms with an Unit Offset and Exponential Term |
| log1pexp |
Logarithms with an Unit Offset and Exponential Term |
| logclink |
Complementary-log Link Function |
| logF |
Natural Exponential Family Generalized Hyperbolic Secant Distribution Family Function |
| logff |
Logarithmic Distribution |
| logistic |
Logistic Distribution Family Function |
| logistic1 |
Logistic Distribution Family Function |
| logitlaplace1 |
Log-Laplace and Logit-Laplace Distribution Family Functions |
| logitlink |
Logit Link Function |
| logitoffsetlink |
Logit-with-an-Offset Link Function |
| loglaplace1 |
Log-Laplace and Logit-Laplace Distribution Family Functions |
| logLik.vlm |
Extract Log-likelihood for VGLMs/VGAMs/etc. |
| loglinb2 |
Loglinear Model for Two Binary Responses |
| loglinb3 |
Loglinear Model for Three Binary Responses |
| loglink |
Log Link Function, and Variants |
| loglog |
Log-log and Log-log-log Link Functions |
| logloglink |
Log-log and Log-log-log Link Functions |
| loglogloglink |
Log-log and Log-log-log Link Functions |
| logneglink |
Log Link Function, and Variants |
| lognormal |
Lognormal Distribution |
| logofflink |
Log Link Function with an Offset |
| Lomax |
The Lomax Distribution |
| lomax |
Lomax Distribution Family Function |
| lqnorm |
Minimizing the L-q norm Family Function |
| lrt.stat |
Likelihood Ratio Test Statistics Evaluated at the Null Values |
| lrt.stat.vlm |
Likelihood Ratio Test Statistics Evaluated at the Null Values |
| lrtest |
Likelihood Ratio Test of Nested Models |
| lrtest_vglm |
Likelihood Ratio Test of Nested Models |
| lv |
Latent Variables |
| lvplot |
Latent Variable Plot |
| lvplot.qrrvglm |
Latent Variable Plot for QO models |
| lvplot.rrvglm |
Latent Variable Plot for RR-VGLMs |
| palap |
The Laplace Distribution |
| Paralogistic |
The Paralogistic Distribution |
| paralogistic |
Paralogistic Distribution Family Function |
| param.names |
Utility Functions for the VGAM Package |
| Pareto |
The Pareto Distribution |
| paretoff |
Pareto and Truncated Pareto Distribution Family Functions |
| ParetoI |
The Pareto(IV/III/II) Distributions |
| ParetoII |
The Pareto(IV/III/II) Distributions |
| paretoII |
Pareto(IV/III/II) Distribution Family Functions |
| ParetoIII |
The Pareto(IV/III/II) Distributions |
| paretoIII |
Pareto(IV/III/II) Distribution Family Functions |
| ParetoIV |
The Pareto(IV/III/II) Distributions |
| paretoIV |
Pareto(IV/III/II) Distribution Family Functions |
| pbenf |
Benford's Distribution |
| pbenini |
The Benini Distribution |
| pbetabinom |
The Beta-Binomial Distribution |
| pbetabinom.ab |
The Beta-Binomial Distribution |
| pbetageom |
The Beta-Geometric Distribution |
| pbetanorm |
The Beta-Normal Distribution |
| pbiamhcop |
Ali-Mikhail-Haq Bivariate Distribution |
| pbifgmcop |
Farlie-Gumbel-Morgenstern's Bivariate Distribution |
| pbifrankcop |
Frank's Bivariate Distribution |
| pbilogis |
Bivariate Logistic Distribution |
| pbinorm |
Bivariate Normal Distribution Cumulative Distribution Function |
| pbinormcop |
Gaussian Copula (Bivariate) Distribution |
| pbiplackcop |
Plackett's Bivariate Copula |
| pbisa |
The Birnbaum-Saunders Distribution |
| pcard |
Cardioid Distribution |
| pdagum |
The Dagum Distribution |
| pdiffzeta |
Differenced Zeta Distribution |
| peexp |
Expectiles of the Exponential Distribution |
| penorm |
Expectiles of the Normal Distribution |
| Perks |
The Perks Distribution |
| perks |
Perks Distribution Family Function |
| perspqrrvglm |
Perspective plot for QRR-VGLMs |
| peunif |
Expectiles of the Uniform Distribution |
| pexpgeom |
The Exponential Geometric Distribution |
| pexplog |
The Exponential Logarithmic Distribution |
| pexppois |
The Exponential Poisson Distribution |
| pfisk |
The Fisk Distribution |
| pfoldnorm |
The Folded-Normal Distribution |
| pfrechet |
The Frechet Distribution |
| pgaitbinom |
Generally-Altered, -Inflated and -Truncated Binomial Distribution |
| pgaitlog |
Generally-Altered, -Inflated and -Truncated Logarithmic Distribution |
| pgaitnbinom |
Generally-Altered, -Inflated and -Truncated Negative Binomial Distribution |
| pgaitpois |
Generally-Altered, -Inflated and -Truncated Poisson Distribution |
| pgaitzeta |
Generally-Altered, -Inflated and -Truncated Zeta Distribution |
| pgamma.deriv |
Derivatives of the Incomplete Gamma Integral |
| pgamma.deriv.unscaled |
Derivatives of the Incomplete Gamma Integral (Unscaled Version) |
| pgengamma.stacy |
Generalized Gamma Distribution |
| pgenpois0 |
Generalized Poisson Distribution (Original Parameterization) |
| pgenpois1 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| pgenpois2 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| pgenray |
The Generalized Rayleigh Distribution |
| pgev |
The Generalized Extreme Value Distribution |
| pgompertz |
Gompertz Distribution |
| pgpd |
The Generalized Pareto Distribution |
| pgumbel |
The Gumbel Distribution |
| pgumbelII |
The Gumbel-II Distribution |
| phuber |
Huber's Least Favourable Distribution |
| phzeta |
Haight's Zeta Distribution |
| pinv.gaussian |
The Inverse Gaussian Distribution |
| pinv.lomax |
The Inverse Lomax Distribution |
| pinv.paralogistic |
The Inverse Paralogistic Distribution |
| pkumar |
The Kumaraswamy Distribution |
| plaplace |
The Laplace Distribution |
| plgamma |
The Log-Gamma Distribution |
| plind |
The Lindley Distribution |
| plino |
The Generalized Beta Distribution (Libby and Novick, 1982) |
| plog |
Logarithmic Distribution |
| ploglap |
The Log-Laplace Distribution |
| plomax |
The Lomax Distribution |
| plot.vgam |
Default VGAM Plotting |
| plotdeplot.lmscreg |
Density Plot for LMS Quantile Regression |
| plotdgait |
Plotting the GAIT Combo Density from a GAIT Regression Object |
| plotdgait.vglm |
Plotting the GAIT Combo Density from a GAIT Regression Object |
| plotqrrvglm |
Model Diagnostic Plots for QRR-VGLMs |
| plotqtplot.lmscreg |
Quantile Plot for LMS Quantile Regression |
| plotrcim0 |
Main Effects Plot for a Row-Column Interaction Model (RCIM) |
| plotvgam |
Default VGAM Plotting |
| plotvgam.control |
Control Function for plotvgam() |
| plotvglm |
Plots for VGLMs |
| pmakeham |
The Makeham Distribution |
| pmaxwell |
The Maxwell Distribution |
| pnaka |
Nakagami Distribution |
| pneumo |
Pneumoconiosis in Coalminers Data |
| pnorm2 |
Bivariate Normal Distribution Cumulative Distribution Function |
| poisson.points |
Poisson-points-on-a-plane/volume Distances Distribution |
| poissonff |
Poisson Regression |
| PoissonPoints |
Poisson Points Distribution |
| Polono |
The Poisson Lognormal Distribution |
| polya |
Negative Binomial Distribution Family Function |
| polyaR |
Negative Binomial Distribution Family Function |
| pordlink |
Poisson-Ordinal Link Function |
| posbernoulli.b |
Positive Bernoulli Family Function with Behavioural Effects |
| posbernoulli.t |
Positive Bernoulli Family Function with Time Effects |
| posbernoulli.tb |
Positive Bernoulli Family Function with Time and Behavioural Effects |
| posbernUC |
Positive Bernoulli Sequence Model |
| posbinomial |
Positive Binomial Distribution Family Function |
| Posgeom |
Positive-Geometric Distribution |
| posnegbinomial |
Positive Negative Binomial Distribution Family Function |
| Posnorm |
The Positive-Normal Distribution |
| posnormal |
Positive Normal Distribution Family Function |
| pospoisson |
Positive Poisson Distribution Family Function |
| powerlink |
Power Link Function |
| pparalogistic |
The Paralogistic Distribution |
| ppareto |
The Pareto Distribution |
| pparetoI |
The Pareto(IV/III/II) Distributions |
| pparetoII |
The Pareto(IV/III/II) Distributions |
| pparetoIII |
The Pareto(IV/III/II) Distributions |
| pparetoIV |
The Pareto(IV/III/II) Distributions |
| pperks |
The Perks Distribution |
| ppolono |
The Poisson Lognormal Distribution |
| pposgeom |
Positive-Geometric Distribution |
| pposnorm |
The Positive-Normal Distribution |
| prats |
Pregnant Rats Toxological Experiment Data |
| prayleigh |
Rayleigh Distribution |
| predictqrrvglm |
Predict Method for a CQO fit |
| predictvglm |
Predict Method for a VGLM fit |
| prentice74 |
Prentice (1974) Log-gamma Distribution |
| price |
The Rice Distribution |
| prinia |
Yellow-bellied Prinia |
| probitlink |
Probit Link Function |
| profilevglm |
Method for Profiling vglm Objects |
| propodds |
Proportional Odds Model for Ordinal Regression |
| prplot |
Probability Plots for Categorical Data Analysis |
| prplot.control |
Probability Plots for Categorical Data Analysis |
| psc.t2 |
Expectiles/Quantiles of the Scaled Student t Distribution with 2 Df |
| psinmad |
The Singh-Maddala Distribution |
| pslash |
Slash Distribution |
| ptobit |
The Tobit Distribution |
| ptopple |
The Topp-Leone Distribution |
| ptriangle |
The Triangle Distribution |
| ptruncpareto |
The Truncated Pareto Distribution |
| put.smart |
Adds a List to the End of the List ".smart.prediction" |
| pyules |
Yule-Simon Distribution |
| pzabinom |
Zero-Altered Binomial Distribution |
| pzageom |
Zero-Altered Geometric Distribution |
| pzanegbin |
Zero-Altered Negative Binomial Distribution |
| pzapois |
Zero-Altered Poisson Distribution |
| pzeta |
The Zeta Distribution |
| pzibinom |
Zero-Inflated Binomial Distribution |
| pzigeom |
Zero-Inflated Geometric Distribution |
| pzinegbin |
Zero-Inflated Negative Binomial Distribution |
| pzipf |
The Zipf Distribution |
| pzipfmb |
The Zipf-Mandelbrot Distribution |
| pzipois |
Zero-Inflated Poisson Distribution |
| pzoabeta |
The Zero/One-Inflated Beta Distribution |
| pzoibetabinom |
The Beta-Binomial Distribution |
| pzoibetabinom.ab |
The Beta-Binomial Distribution |
| qalap |
The Laplace Distribution |
| qbenf |
Benford's Distribution |
| qbenini |
The Benini Distribution |
| qbetanorm |
The Beta-Normal Distribution |
| qbisa |
The Birnbaum-Saunders Distribution |
| qcard |
Cardioid Distribution |
| qdagum |
The Dagum Distribution |
| qdiffzeta |
Differenced Zeta Distribution |
| qeexp |
Expectiles of the Exponential Distribution |
| qenorm |
Expectiles of the Normal Distribution |
| qeunif |
Expectiles of the Uniform Distribution |
| qexpgeom |
The Exponential Geometric Distribution |
| qexplog |
The Exponential Logarithmic Distribution |
| qexppois |
The Exponential Poisson Distribution |
| qfisk |
The Fisk Distribution |
| qfoldnorm |
The Folded-Normal Distribution |
| qfrechet |
The Frechet Distribution |
| qgaitbinom |
Generally-Altered, -Inflated and -Truncated Binomial Distribution |
| qgaitlog |
Generally-Altered, -Inflated and -Truncated Logarithmic Distribution |
| qgaitnbinom |
Generally-Altered, -Inflated and -Truncated Negative Binomial Distribution |
| qgaitpois |
Generally-Altered, -Inflated and -Truncated Poisson Distribution |
| qgaitzeta |
Generally-Altered, -Inflated and -Truncated Zeta Distribution |
| qgengamma.stacy |
Generalized Gamma Distribution |
| qgenpois0 |
Generalized Poisson Distribution (Original Parameterization) |
| qgenpois1 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| qgenpois2 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| qgenray |
The Generalized Rayleigh Distribution |
| qgev |
The Generalized Extreme Value Distribution |
| qgompertz |
Gompertz Distribution |
| qgpd |
The Generalized Pareto Distribution |
| qgumbel |
The Gumbel Distribution |
| qgumbelII |
The Gumbel-II Distribution |
| qhuber |
Huber's Least Favourable Distribution |
| qhzeta |
Haight's Zeta Distribution |
| qinv.lomax |
The Inverse Lomax Distribution |
| qinv.paralogistic |
The Inverse Paralogistic Distribution |
| qkumar |
The Kumaraswamy Distribution |
| qlaplace |
The Laplace Distribution |
| qlgamma |
The Log-Gamma Distribution |
| qlino |
The Generalized Beta Distribution (Libby and Novick, 1982) |
| qlog |
Logarithmic Distribution |
| qloglap |
The Log-Laplace Distribution |
| qlomax |
The Lomax Distribution |
| qmakeham |
The Makeham Distribution |
| qmaxwell |
The Maxwell Distribution |
| qnaka |
Nakagami Distribution |
| qparalogistic |
The Paralogistic Distribution |
| qpareto |
The Pareto Distribution |
| qparetoI |
The Pareto(IV/III/II) Distributions |
| qparetoII |
The Pareto(IV/III/II) Distributions |
| qparetoIII |
The Pareto(IV/III/II) Distributions |
| qparetoIV |
The Pareto(IV/III/II) Distributions |
| qperks |
The Perks Distribution |
| qposgeom |
Positive-Geometric Distribution |
| qposnorm |
The Positive-Normal Distribution |
| qrayleigh |
Rayleigh Distribution |
| qrice |
The Rice Distribution |
| qrrvglm.control |
Control Function for QRR-VGLMs (CQO) |
| qsc.t2 |
Expectiles/Quantiles of the Scaled Student t Distribution with 2 Df |
| qsinmad |
The Singh-Maddala Distribution |
| qtobit |
The Tobit Distribution |
| qtopple |
The Topp-Leone Distribution |
| qtplot.gumbel |
Quantile Plot for Gumbel Regression |
| qtplot.gumbelff |
Quantile Plot for Gumbel Regression |
| qtplot.lmscreg |
Quantile Plot for LMS Quantile Regression |
| qtriangle |
The Triangle Distribution |
| qtruncpareto |
The Truncated Pareto Distribution |
| Qvar |
Quasi-variances Preprocessing Function |
| qvar |
Quasi-variances Extraction Function |
| qyules |
Yule-Simon Distribution |
| qzabinom |
Zero-Altered Binomial Distribution |
| qzageom |
Zero-Altered Geometric Distribution |
| qzanegbin |
Zero-Altered Negative Binomial Distribution |
| qzapois |
Zero-Altered Poisson Distribution |
| qzeta |
The Zeta Distribution |
| qzibinom |
Zero-Inflated Binomial Distribution |
| qzigeom |
Zero-Inflated Geometric Distribution |
| qzinegbin |
Zero-Inflated Negative Binomial Distribution |
| qzipf |
The Zipf Distribution |
| qzipfmb |
The Zipf-Mandelbrot Distribution |
| qzipois |
Zero-Inflated Poisson Distribution |
| qzoabeta |
The Zero/One-Inflated Beta Distribution |
| R2latvar |
R-squared for Latent Variable Models |
| ralap |
The Laplace Distribution |
| Rank |
Rank |
| Rank.qrrvglm |
Rank |
| Rank.rrvgam |
Rank |
| Rank.rrvglm |
Rank |
| Rayleigh |
Rayleigh Distribution |
| rayleigh |
Rayleigh Regression Family Function |
| rbenf |
Benford's Distribution |
| rbenini |
The Benini Distribution |
| rbetabinom |
The Beta-Binomial Distribution |
| rbetabinom.ab |
The Beta-Binomial Distribution |
| rbetageom |
The Beta-Geometric Distribution |
| rbetanorm |
The Beta-Normal Distribution |
| rbiamhcop |
Ali-Mikhail-Haq Bivariate Distribution |
| rbiclaytoncop |
Clayton Copula (Bivariate) Distribution |
| rbifgmcop |
Farlie-Gumbel-Morgenstern's Bivariate Distribution |
| rbifrankcop |
Frank's Bivariate Distribution |
| rbilogis |
Bivariate Logistic Distribution |
| rbinom2.or |
Bivariate Odds Ratio Model |
| rbinom2.rho |
Bivariate Probit Model |
| rbinorm |
Bivariate Normal Distribution Cumulative Distribution Function |
| rbinormcop |
Gaussian Copula (Bivariate) Distribution |
| rbiplackcop |
Plackett's Bivariate Copula |
| rbisa |
The Birnbaum-Saunders Distribution |
| rbort |
The Borel-Tanner Distribution |
| rcard |
Cardioid Distribution |
| Rcim |
Mark the Baseline of Row and Column on a Matrix data |
| rcim |
Row-Column Interaction Models including Goodman's RC Association Model and Unconstrained Quadratic Ordination |
| rcqo |
Constrained Quadratic Ordination |
| rdagum |
The Dagum Distribution |
| rdiffzeta |
Differenced Zeta Distribution |
| rdiric |
The Dirichlet distribution |
| rec.exp1 |
Upper Record Values from a 1-parameter Exponential Distribution |
| rec.normal |
Upper Record Values from a Univariate Normal Distribution |
| reciprocallink |
Reciprocal Link Function |
| reexp |
Expectiles of the Exponential Distribution |
| renorm |
Expectiles of the Normal Distribution |
| residualsvglm |
Residuals for a VGLM fit |
| reunif |
Expectiles of the Uniform Distribution |
| rexpgeom |
The Exponential Geometric Distribution |
| rexplog |
The Exponential Logarithmic Distribution |
| rexppois |
The Exponential Poisson Distribution |
| rfisk |
The Fisk Distribution |
| rfoldnorm |
The Folded-Normal Distribution |
| rfrechet |
The Frechet Distribution |
| rgaitbinom |
Generally-Altered, -Inflated and -Truncated Binomial Distribution |
| rgaitlog |
Generally-Altered, -Inflated and -Truncated Logarithmic Distribution |
| rgaitnbinom |
Generally-Altered, -Inflated and -Truncated Negative Binomial Distribution |
| rgaitpois |
Generally-Altered, -Inflated and -Truncated Poisson Distribution |
| rgaitzeta |
Generally-Altered, -Inflated and -Truncated Zeta Distribution |
| rgengamma.stacy |
Generalized Gamma Distribution |
| rgenpois0 |
Generalized Poisson Distribution (Original Parameterization) |
| rgenpois1 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| rgenpois2 |
Generalized Poisson Distribution (GP-1 and GP-2 Parameterizations of the Mean) |
| rgenray |
The Generalized Rayleigh Distribution |
| rgev |
The Generalized Extreme Value Distribution |
| rgompertz |
Gompertz Distribution |
| rgpd |
The Generalized Pareto Distribution |
| rgumbel |
The Gumbel Distribution |
| rgumbelII |
The Gumbel-II Distribution |
| rhobitlink |
Rhobit Link Function |
| rhuber |
Huber's Least Favourable Distribution |
| rhzeta |
Haight's Zeta Distribution |
| Rice |
The Rice Distribution |
| riceff |
Rice Distribution Family Function |
| rigff |
Reciprocal Inverse Gaussian distribution |
| rinv.gaussian |
The Inverse Gaussian Distribution |
| rinv.lomax |
The Inverse Lomax Distribution |
| rinv.paralogistic |
The Inverse Paralogistic Distribution |
| rkumar |
The Kumaraswamy Distribution |
| rlaplace |
The Laplace Distribution |
| rlgamma |
The Log-Gamma Distribution |
| rlind |
The Lindley Distribution |
| rlino |
The Generalized Beta Distribution (Libby and Novick, 1982) |
| rlog |
Logarithmic Distribution |
| rloglap |
The Log-Laplace Distribution |
| rlomax |
The Lomax Distribution |
| rlplot.gev |
Return Level Plot for GEV Fits |
| rlplot.gevff |
Return Level Plot for GEV Fits |
| rmakeham |
The Makeham Distribution |
| rmaxwell |
The Maxwell Distribution |
| rnaka |
Nakagami Distribution |
| rootogram4 |
Rootograms (S4 generic) for Assessing Goodness of Fit of Probability Models |
| rootogram4vglm |
Rootograms (S4 generic) for Assessing Goodness of Fit of Probability Models |
| round2 |
Rounding of Numbers to Base 2 |
| rparalogistic |
The Paralogistic Distribution |
| rpareto |
The Pareto Distribution |
| rparetoI |
The Pareto(IV/III/II) Distributions |
| rparetoII |
The Pareto(IV/III/II) Distributions |
| rparetoIII |
The Pareto(IV/III/II) Distributions |
| rparetoIV |
The Pareto(IV/III/II) Distributions |
| rperks |
The Perks Distribution |
| rpois.points |
Poisson Points Distribution |
| rpolono |
The Poisson Lognormal Distribution |
| rposbern |
Positive Bernoulli Sequence Model |
| rposgeom |
Positive-Geometric Distribution |
| rposnorm |
The Positive-Normal Distribution |
| rrar |
Nested Reduced-rank Autoregressive Models for Multiple Time Series |
| rrayleigh |
Rayleigh Distribution |
| rrice |
The Rice Distribution |
| rrvglm |
Fitting Reduced-Rank Vector Generalized Linear Models (RR-VGLMs) |
| rrvglm-class |
Class "rrvglm" |
| rrvglm.control |
Control Function for rrvglm() |
| rrvglm.optim.control |
Control Function for rrvglm() Calling optim() |
| rsc.t2 |
Expectiles/Quantiles of the Scaled Student t Distribution with 2 Df |
| rsimplex |
Simplex Distribution |
| rsinmad |
The Singh-Maddala Distribution |
| rskellam |
The Skellam Distribution |
| rskewnorm |
Skew-Normal Distribution |
| rslash |
Slash Distribution |
| rtobit |
The Tobit Distribution |
| rtopple |
The Topp-Leone Distribution |
| rtriangle |
The Triangle Distribution |
| rtrinorm |
Trivariate Normal Distribution Density and Random Variates |
| rtruncpareto |
The Truncated Pareto Distribution |
| ruge |
Rutherford-Geiger Polonium Data |
| ryules |
Yule-Simon Distribution |
| rzabinom |
Zero-Altered Binomial Distribution |
| rzageom |
Zero-Altered Geometric Distribution |
| rzanegbin |
Zero-Altered Negative Binomial Distribution |
| rzapois |
Zero-Altered Poisson Distribution |
| rzeta |
The Zeta Distribution |
| rzibinom |
Zero-Inflated Binomial Distribution |
| rzigeom |
Zero-Inflated Geometric Distribution |
| rzinegbin |
Zero-Inflated Negative Binomial Distribution |
| rzipf |
The Zipf Distribution |
| rzipfmb |
The Zipf-Mandelbrot Distribution |
| rzipois |
Zero-Inflated Poisson Distribution |
| rzoabeta |
The Zero/One-Inflated Beta Distribution |
| rzoibetabinom |
The Beta-Binomial Distribution |
| rzoibetabinom.ab |
The Beta-Binomial Distribution |
| s |
Defining Smooths in VGAM Formulas |
| sc.studentt2 |
Scaled Student t Distribution with 2 df Family Function |
| score.stat |
Rao's Score Test Statistics Evaluated at the Null Values |
| score.stat.vlm |
Rao's Score Test Statistics Evaluated at the Null Values |
| seglines |
Hauck-Donner Effects: Segmented Lines Plot |
| Select |
Select Variables for a Formula Response or the RHS of a Formula |
| seq2binomial |
The Two-stage Sequential Binomial Distribution Family Function |
| setup.smart |
Smart Prediction Setup |
| show-method |
Class "SurvS4" |
| show.summary.pvgam |
Summarizing Penalized Vector Generalized Additive Model Fits |
| show.summary.vgam |
Summarizing Vector Generalized Additive Model Fits |
| show.summary.vglm |
Summarizing Vector Generalized Linear Model Fits |
| show.SurvS4 |
Create a Survival Object |
| simplex |
Simplex Distribution Family Function |
| simulate.vlm |
Simulate Responses for VGLMs and VGAMs |
| Sinmad |
The Singh-Maddala Distribution |
| sinmad |
Singh-Maddala Distribution Family Function |
| Skellam |
The Skellam Distribution |
| skellam |
Skellam Distribution Family Function |
| skewnorm |
Skew-Normal Distribution |
| skewnormal |
Univariate Skew-Normal Distribution Family Function |
| Slash |
Slash Distribution |
| slash |
Slash Distribution Family Function |
| sm.bs |
Smart Prediction |
| sm.ns |
Smart Prediction |
| sm.os |
Defining O'Sullivan Spline Smooths in VGAM Formulas |
| sm.poly |
Smart Prediction |
| sm.ps |
Defining Penalized Spline Smooths in VGAM Formulas |
| sm.scale |
Smart Prediction |
| sm.scale.default |
Smart Prediction |
| smart.expression |
S Expression for Smart Functions |
| smart.mode.is |
Determine What Mode the Smart Prediction is In |
| smartpred |
Smart Prediction |
| specials |
Special Values or Quantities in a Fitted Object |
| specialsvglm |
Special Values or Quantities in a Fitted Object |
| spikeplot |
Spike Plot |
| sratio |
Ordinal Regression with Stopping Ratios |
| step4 |
Choose a model by AIC in a Stepwise Algorithm |
| step4vglm |
Choose a model by AIC in a Stepwise Algorithm |
| studentt |
Student t Distribution |
| studentt2 |
Student t Distribution |
| studentt3 |
Student t Distribution |
| subsetcol |
Select Variables for a Formula Response or the RHS of a Formula |
| Summary.SurvS4 |
Create a Survival Object |
| summarypvgam |
Summarizing Penalized Vector Generalized Additive Model Fits |
| summaryvgam |
Summarizing Vector Generalized Additive Model Fits |
| summaryvglm |
Summarizing Vector Generalized Linear Model Fits |
| SURff |
Seemingly Unrelated Regressions Family Function |
| SurvS4 |
Create a Survival Object |
| SurvS4-class |
Class "SurvS4" |
| Zabinom |
Zero-Altered Binomial Distribution |
| zabinomial |
Zero-Altered Binomial Distribution |
| zabinomialff |
Zero-Altered Binomial Distribution |
| Zageom |
Zero-Altered Geometric Distribution |
| zageometric |
Zero-Altered Geometric Distribution |
| zageometricff |
Zero-Altered Geometric Distribution |
| Zanegbin |
Zero-Altered Negative Binomial Distribution |
| zanegbinomial |
Zero-Altered Negative Binomial Distribution |
| zanegbinomialff |
Zero-Altered Negative Binomial Distribution |
| Zapois |
Zero-Altered Poisson Distribution |
| zapoisson |
Zero-Altered Poisson Distribution |
| zapoissonff |
Zero-Altered Poisson Distribution |
| zero |
The zero Argument in VGAM Family Functions |
| Zeta |
The Zeta Distribution |
| zeta |
Riemann's Zeta Function |
| zetaff |
Zeta Distribution Family Function |
| Zibinom |
Zero-Inflated Binomial Distribution |
| zibinomial |
Zero-Inflated Binomial Distribution Family Function |
| zibinomialff |
Zero-Inflated Binomial Distribution Family Function |
| Zigeom |
Zero-Inflated Geometric Distribution |
| zigeometric |
Zero-Inflated Geometric Distribution Family Function |
| zigeometricff |
Zero-Inflated Geometric Distribution Family Function |
| Zinegbin |
Zero-Inflated Negative Binomial Distribution |
| zinegbinomial |
Zero-Inflated Negative Binomial Distribution Family Function |
| zinegbinomialff |
Zero-Inflated Negative Binomial Distribution Family Function |
| zipebcom |
Exchangeable Bivariate cloglog Odds-ratio Model From a Zero-inflated Poisson Distribution |
| Zipf |
The Zipf Distribution |
| zipf |
Zipf Distribution Family Function |
| Zipfmb |
The Zipf-Mandelbrot Distribution |
| Zipois |
Zero-Inflated Poisson Distribution |
| zipoisson |
Zero-Inflated Poisson Distribution Family Function |
| zipoissonff |
Zero-Inflated Poisson Distribution Family Function |
| Zoabeta |
The Zero/One-Inflated Beta Distribution |
| zoabetaR |
Zero- and One-Inflated Beta Distribution Family Function |


This document provides incident reporting guidelines and an enforcement manual for the following codes of conduct used by Creative Commons:
If you believe someone is violating the code of conduct, we ask that you report it to Creative Commons by emailing<EMAIL_ADDRESS>All reports will be kept confidential. In some cases we may determine that a public statement will need to be made, which the committee may make in its discretion. The identities of all victims and reporters will remain confidential unless those individuals instruct us otherwise.
If you believe anyone is in physical danger, please notify appropriate emergency personnel first. If you are unsure what proper authorities are appropriate, please include this in your report and we will attempt to notify them.
If you are unsure whether the incident is a violation, or whether the space where it happened is covered by this Code of Conduct, we still encourage you to report it. We would much rather have a few extra reports where we decide to take no action, rather than miss a report of an actual violation. We do not look negatively on you if we find the incident is not a violation. Knowing about incidents that are not violations, or happen outside our spaces, can also help us to improve the Code of Conduct or the processes surrounding it.
In your report please include:
In the event of any conflict of interest a committee member must immediately notify the other members, and recuse themselves if the other committee members believe a conflict exists. If a report concerns a possible violation by a current committee member, this member must be excluded from the response process. For these cases, anyone can make a report directly to the CC Legal team at<EMAIL_ADDRESS>instead of through the regular process, above.
This manual mainly covers reports of code of conduct violations from community members. If an administrator of a forum notices ongoing or dangerous behavior that violates the code of conduct (such as spam), they may act unilaterally as described in the Acting Unilaterally section below.
All responses to reports of conduct violations will be managed by a Code of Conduct Committee ("the committee"), which will be comprised of three members of Creative Commons staff. The Code of Conduct Committee will serve in the role of "project maintainers" as described in the Contributor Covenant.
When a report is sent to the committee they will reply promptly, and within no more than 2 Creative Commons business days.
See the reporting guidelines above for details of what reports should contain. If a report doesn't contain enough information, the committee will obtain all relevant data before acting.
The committee will then review the incident and determine, to the best of their ability:
The committee should aim to have a resolution agreed upon within one week. In the event that a resolution can't be determined in that time, the committee will respond to the reporter(s) with an update and projected timeline for resolution.
Not every situation acted upon by the Code of Conduct Committee will be the result of a report from the community. In some cases, a Code of Conduct Committee member will witness a violation themselves. If the act is ongoing (such as someone engaging in harassment in an IRC channel or spamming a Slack channel), or involves a threat to anyone's safety (e.g. threats of violence), any committee member may act immediately (before reaching consensus) to end the situation. In ongoing situations, any member may at their discretion employ any of the tools available to the committee, including bans and blocks.
If the incident involves physical danger, any member of the committee may -- and should -- act unilaterally to protect safety. This can include contacting appropriate emergency personnel first or speaking on behalf of Creative Commons.
In situations where an individual committee member acts unilaterally, they must report their actions to the committee for review within 1 business day.
The committee will follow the Community Impact Guidelines, below, in determining the consequences for any action they deem in violation of this Code of Conduct.
The committee should strive to agree on a resolution by consensus (i.e. no one on the committee chooses to veto the course of action). As soon as the committee determines it is unable to reach consensus or it has been deadlocked for over 2 business days after acknowledging receipt of the issue, the committee will turn the matter over to the CC legal team for resolution.
Once a resolution is agreed upon, but before it is enacted, the committee will contact the original reporter and any other affected parties and explain the proposed resolution. The committee will ask if this resolution is acceptable, and must note feedback for the record. However, the committee is not required to act on this feedback.
Community Impact: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.
Consequence: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.
Community Impact: A violation through a single incident or series of actions.
Consequence: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.
Community Impact: A serious violation of community standards, including sustained inappropriate behavior.
Consequence: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
Community Impact: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
Consequence: A permanent ban from any sort of public interaction within the community.
Any of the parties directly involved or affected can request reconsideration of the committee’s decision. To make such a request, contact the CC Legal team at<EMAIL_ADDRESS>with your request and reason and they will review the case.
This policy may be revised and updated by Creative Commons from time to time. You should not participate in our digital communities unless you agree with and comply with the current policy.
This document is adapted from the Django project's Code of Conduct enforcement manual and reporting guidelines, licensed under CC BY 3.0 and the Contributor Covenant, version 2.1, available at https://www.contributor-covenant.org/version/2/1/code_of_conduct.html.
Community Impact Guidelines were inspired by Mozilla's code of conduct enforcement ladder.


GitPals
How to create good description for your project
When you are about to submit your project,
you should check twice that description describes it the best.
- 1. Make sure you have chosen all the requirements (skills) for the project you really need
- 2. Describe how much time you will be spending on this project.
- 3. Provide your Email, Skype, Discord or any other service where you will be discussing everything about the project.
- 4. Provide deadline for your project
- 5. Provide any working concept as an example (if you have, of course)
- 6. Mention if this is a profitable or non-profitable project.
Good luck to you!