The following is a general changelog for Pikifen. This changelog will only list the most notable differences between each version of the engine; for a more detailed list of changes throughout the development period, check the commit log on GitHub.
Some versions may be unable to read content created in older versions. If you want to keep your content from one version to the next, read the "Content making changes" list in that update's section.
Versions are numbered after the format major.minor.revision
. The major number increases when the project is radically different from the major version before it. The minor number increases every time there are a bunch of changes. The revision number increases when there are bug fixes on the revision before it, and does not break compatibility unless that's necessary to fix something.
0.25.0
Under development
Gameplay changes
- Changed how some objects push against each other. Now, leaders and Pikmin can squeeze through them to get unstuck. This does not apply to more "solid" objects like gates.
- Changed the range for an idle Pikmin to be considered "bumped" by the active leader, so they can be called from farther away.
Content making changes
- Added the
pushes_softly
property to mob types. - Added support for the thus far-unused
maturity_power_mult
property of the game configuration. - Added the
idle_bump_range
property to the game configuration.
0.24.0
July 14th, 2023 – the "new areas" update
Gameplay changes
-
New areas:
- Added Tutorial Meadow. If you never played an area before, you will be prompted to start here.
- Added Cryptic Atrium. A medium-short puzzle stage with pressure plates and automatic gates.
- Added Playful Yard. A reincarnation of the old "Play" area, with a more playful motif, better design, and as a proper mission.
- Added Crash Landing. A difficult quick mission to get all treasures before time runs out. (Thanks Helodity)
- Added DEV Lab. It showcases pretty much everything Pikifen has to offer.
- Removed Play in favor of Playful Yard.
-
New objects:
- Added orb signs, which work like signs but are generic orbs instead of wooden planks.
- Added geysers, a type of bouncer that only leaders can use.
- Added carriable rocks, which can be carried around and dropped to weigh on something.
- Added pressure plates, which trigger some other object to do something whenever they have enough weight on them.
- Added automatic gates, which lift in the air whenever they are powered by things like a pressure plate.
- Added carry targets, which allows other things to be carried to it for no effect.
- Added some treasures: a fidget spinner, a yo-yo, and a jigsaw puzzle piece.
-
Misc:
- Added images to the help page of the pause menu. (Thanks erdinvark)
- Added a random area button to the area selection menu.
- Added mission statistics for the total of clears, total of platinum medals, and total combined sum of score points.
- Changed the way paper bags work to make it so leaders can't be thrown on top, and so that Pikmin can't accidentally be thrown over them to the other side.
Content making changes
- Editor improvements:
- Added a bunch of menu bar items across all editors.
- Added a toolbar button to the area editor to toggle preview mode. Advanced interface mode only.
- Added the ability to lock to an axis while dragging, if Ctrl is held.
- Added the ability to add to the selection when dragging a selection box, if Ctrl is held.
- Added the ability to copy a sector, edge, object, or path link's properties, so they can then be pasted elsewhere.
- Added some shortcut operations to the sector height widget as a right-click context menu.
- Added settings for the area editor's path preview tool. Plus some more detailed information about the path calculation.
- Changed the unsaved changes warning to be a dialog like in most programs. The idea of the old pop-up was to require fewer clicks and mouse movement when you were sure you wanted to not save, but some actions are hidden behind menus which forces you to go there twice. The dialog approach comes with many quality-of-life tweaks that make it an overall better feature.
- Changed the way copying textures from a different sector works. Instead of selecting the source, then hovering the mouse over the destination and pressing T, you now copy the source sector's properties, then paste just the texture on the selected sectors.
- Changed what happens when the user is drawing a new sector and clicks on an edge or vertex. Instead of throwing an error, the editor now changes the drawing to a sector split drawing, and lets the user continue drawing from the opposite end.
- Changed the area editor's saving procedure to no longer need to boot the user back to the main panel.
- Others:
- Added the
hud
maker tool. - Added the
path_info
maker tool. - Added the
custom_carry_spots
property to mob types. - Added the
power_goal
script variable to group tasks. - Added the
goal_number
script variable to scales. - Added the
stay
value to a resource type's delivery results. - Added the
linked_mob_matching_type
value to a resource type's carrying destinations. - Added the
area_image_padding
property to the maker tools. - Added the ability for dead enemies to drop objects via a "stored inside" feature in the area editor. This allows the engine to detect things like treasures inside enemies for the purposes of missions.
- Added the
Breaking changes
- Removed the ability for dead enemies to drop objects via the
spoils
andrandom_pellet_spoils
script variables, in favor of the "stored inside" area editor feature. Areas with such objects need to be adjusted. - Changed the following obstacles to have a consistent 192px size: electrical wire (also now doesn't push), paper bag (also now blocks paths), cardboard box, wood bridge.
- Changed the spritesheet for player input icons (formerly control icons). All properties and files changed from
control_icon
toplayer_input_icon
. - Changed the
show_hud_controls
option toshow_hud_input_icons
. - Changed the gameplay GUI item names that had
button
in them to instead haveinput
. - Changed the default value of an object type's
can_walk_on_others
properties to false. - Changed the name of some texture files:
Bark
→Wood_bark
.Bridge
→Wood_bridge
.Bridge_rail
→Wood_bridge_rail
.Metal
→Metal_rusted
.Stump
→Wood_stump
.
0.23.2
January 23rd, 2023
Bug fixes and improvements
- Fixed bouncers not working. (Thanks Darthvato)
- Fixed Pikmin taking paths that they are vulnerable to. (Thanks Quarter C++)
- Fixed the camera sometimes focusing on the wrong spot when a "get to the exit" mission is completed. (Thanks Helodity)
- Fixed dropped bomb rocks not activating if the Pikmin hits an opponent mid-throw. (Thanks erdinvark)
- Fixed leaders being unable to throw Pikmin if either one is under a lifted bamboo gate.
- Fixed the gameplay start day time area property not being respected. (Thanks Helodity)
- Fixed the weather tables needing an entry for 0. (Thanks Helodity)
- Fixed the calculation for seconds remaining, in the results screen.
0.23.1
January 1st, 2023
Breaking changes
- Changed how the mission records and statistics files are saved and loaded. This fixes a problem where the files wouldn't be read if they had certain characters. When you start 0.23.1, these files from 0.23.0, if any, will be deleted. If you really want to keep their content, get in contact with me by reading
README.txt
.
Bug fixes and improvements
- Fixed the edge length indicators not appearing in the area editor. (Thanks Helodity)
- Fixed negative point amounts for mission criteria not saving. (Thanks Helodity)
- Fixed the HUD using enemy kills when the mission specifies using enemy points instead. (Thanks Helodity)
- Fixed wall shadows not rendering when there are liquids in the area editor's preview. (Thanks Helodity)
- Fixed sectors not showing their liquid in the area editor's preview unless you reload the area. (Thanks Helodity)
- Fixed the area selection menu always showing a platinum medal next to the area name, if you have a medal. (Thanks Helodity)
- Fixed a crash when one selects a GUI item with no behavior and presses Enter.
- Fixed the results menu showing the centiseconds digit instead of the deciseconds digit.
- Changed the way gameplay time is calculated, so time spent during interludes (the starting countdown, mission end text, etc.) doesn't count. (Thanks Helodity)
0.23.0
December 25th, 2022 – the "Mission Mode" update
Gameplay changes
-
Missions:
- Added mission-type areas. Gameplay during missions usually involves reaching a goal, avoiding some fail conditions, and getting medals based on several scoring criteria.
- Added info about the area that is currently selected in the list, in the area selection screen.
- Added a mission information tab to the pause menu, if you're playing on a mission.
- Added a "play" button and a "make" button for the main menu, which show the list of area types that can be played, and the list of editors, respectively.
- Changed the results screen to better suit a mission's completion screen.
-
Lifetime statistics:
- Added lifetime statistics, accessible from a menu in the main menu. These are saved onto the disk.
-
Others:
- Added the ability to play when there are no available leaders, or even if there were none to begin with. There isn't much to do in this state other than move the camera, however.
- Added the ability for the camera to be influenced by the group and the cursor. Added an option to control how much the cursor influences it.
- Added a confirmation message for the pause menu, shown when you want to leave. This can be changed in the options. (Thanks Neo)
- Changed the pause menu with various improvements.
Content making changes
-
Area editor improvements:
- Added a history of opened folders.
- Added a "gameplay" panel, with the starting spray amounts (with a better interface), as well as the new mission stuff.
- Added description, tags, thumbnail, and difficulty properties for areas.
- Added a show path link length option. (Thanks Nataly)
- Added a show circular sector info option. (Thanks Neo)
- Added a basic/advanced interface mode option.
- Added the ability to choose what type and label new path links will have as you're drawing them. (Thanks Nataly)
- Added highlights to a sector, mob, etc. when you hover your mouse over it. (Thanks Helodity)
- Added toolbar buttons to swap to the layout, objects, paths, or details mode. Advanced interface mode only. (Thanks Nataly)
- Added the H shortcut key to change a sector's height.
- Added the T shortcut key to copy a sector's texture.
- Added the L, O, P, D shortcut keys to swap to the layout, objects, paths, and details mode. Advanced interface mode only. (Thanks Nataly)
- Added liquids to the area preview feature.
- Added the ability for areas to specify at what point of the in-game day they start, as well as day time speed or (for missions with time limit) the time of day they end.
- Added the Ctrl+F12 shortcut to change the auto-start maker tool's data to match the current mode and currently open content.
- Added a "show collision" maker tool. (Thanks Helodity)
- Added a
points
property for enemy types. - Added a
states_ignoring_hazard
property for scripts. - Added a
hold_offset_vertical_distance
property for a mob type's children data. - Added a
custom_category_name
property for mob types. With this, the pre-packaged mob types are now better organized in the editors. - Added a
can_walk_on_others
property for mob types. - Added a
knocked_down_duration
and aknocked_down_whistle_bonus
property for Pikmin types. - Added an
auto_shrink_smallest_radius
property for pile types. - Added an
interval_deviation
property for particle generators.
- Added a GUI editor. This should make it easier to position and resize GUI elements in menus and HUDs.
- Added the ability to lock the center (as opposed to the opposite side/corner) by holding Alt, when you are dragging a side/corner handle of a transformation widget in an editor's canvas.
- Added script variables to decoration objects to allow them to disable the random property changes.
- Added the
gentle_release
script variable to tool objects so they can know if the Pikmin released it gently (i.e. the Pikmin is entering an Onion) or not. - Added logic for rectangular objects to be able to push other rectangular objects. (Thanks Helodity)
- Changed the duplicate mobs area editor keyboard shortcut from D to Ctrl+D, since D is now used to go to the details mode.
- Changed keyboard shortcuts made for the editor canvases to make them now work anywhere so long as you aren't writing in a text box. (Thanks Neo)
- Changed drag buttons in editors to only need a single click in order to enter text input mode.
Custom content compatibility warnings
-
Revamped the following GUIs:
- Changed the main menu's GUI.
- Changed the area menu's GUI.
- Changed the results screen's GUI.
- Changed the pause menu's GUI.
-
Others:
- Changed the location of area folders, where simple areas used to be in
Game_data/Areas/<folder>
, but must now be moved toGame_data/Areas/Simple/<folder>
. If you care about auto-backups and reference image data, do the same to the area-related stuff in theUser_data
folder. - Removed the
day_minutes_per_irl_sec
field from the config file since now each area controls its speed. - Removed the system asset for the Pikmin silhouette, in favor of two leader silhouettes.
- Added two leader silhouette graphics to replace the Pikmin silhouette image.
- Added a
standard_leader_height
and astandard_leader_radius
game config property.
- Changed the location of area folders, where simple areas used to be in
0.22.0
July 5th, 2022 – the "polish" update
Gameplay changes
- Added a help page to the pause menu, with gameplay information, tips, controls, Pikmin type explanations, and more.
- White Pikmin poison now applies a status effect that drains health over time. Enemies still end up losing the same amount of health in the end.
- Pikmin are now better at detecting gates and other rectangular objects.
- Pikmin can now circle around enemies when in combat.
- Bridges now grow in chunks, and the point Pikmin deliver fragments to, or the position of the pile of sticks, now moves along with the bridge.
-
Misc. graphical enhancements:
- All gates have received graphical improvements.
- Decorative objects no longer cast light in blackout weather.
- When Pikmin deliver an object to an Onion, ship, etc., the sucking animation is now more dynamic. Also, added a tossing animation for delivering bridge fragments to bridges.
- Improved the look of the ship's beam and the leader's whistle. The
pretty_whistle
option has been removed. (Thanks Helodity) - HUD elements now animate when things change, and slide off-frame when menus are entered in-game. (Thanks Helodity)
- In-game message boxes are now much more animated.
- Health wheels and fraction numbers now appear and disappear smoothly, and are more animated in general.
- Enemies now squash and stretch when damaged.
- Objects being carried now sway.
- Icons for controls have been improved.
- Fixed leaders being able to grab group members from the other side of walls or objects with collision, like gates.
Content making changes
- Bridges are now made up of objects. This should make the process of placing bridges in an area much simpler, since there's no need to fiddle around with sectors. Areas with bridges need to be updated to use this new bridge format.
- Status effects now have a
health_change
property, which controls how much health the object loses per second in an absolute value, instead of it being a ratio of total health. - Spike damage types now have a
status_to_apply
property, which allows the damaged object to receive a status effect. - Object types now have a
status_vulnerabilities
block, which works like the other vulnerability blocks, but controls damage taken from status effects. - Object types now have a
terrain_radius
property, which controls their collision against walls. - Object types now have a
description
property, which can be used to briefly describe it. This appears in the area editor, and Pikmin types make use of this for the help page of the in-game pause menu. - The tooltip of an object type's area editor property now word-wraps automatically. If you relied on line breaks (
\n
) to do this before, you may want to adjust it. - The
set_animation
script action now accepts the optionsrandom_time
andrandom_time_on_spawn
, to let the animation start on a random time, or start on a random time but only if the mob just spawned. - Added the
on_receive_delivery
object script event. - Added the
health_ratio
option to theget_info
object script action. - Added the
health
script variable. - Added the
max_health
script variable. - Added the
bright_circle
andbright_ring
system assets. - The area editor now also shows the terrain radius of selected objects (if the "Show territory/terrain radius" option is on).
- Converters now use an animation called
bumped
instead ofbumping
, for consistency's sake. - System assets no longer have icons for mouse-related controls, but instead have one spreadsheet for many special control icons.
- The gameplay HUD received all sorts of new items, and some have been renamed to be more consistent.
- Areas and animations now save the engine version they were edited in. This will likely be used in the future to allow the engine to automatically port older content to newer versions.
Older
Older entries can be found here.


Across: 1. Deletes criticisms after the first (5) 4. Start to look into removing volatile borders (9) 9. That framework's outcome (9) 10. They've decided to keep what they have, something forbidden with previous leader (3-2) 11,6dn. 27 leader, first of 3 then of 14 down 4 down and Football Association, is a liberal getting state of Manchester (USA) on board (6 3 6) 12. More likely to be surviving or bouncing back? Keaton perhaps (8) 14. Plan A: to ask modern leader to return historic territory (11) 16. Part of treaty to unfasten? (3) 17. Acknowledgement when boss returned (3) 19. Playfully taunt senile officers (11) 21. A politician essentially let anger flow (8) 22. Flowing into Persian Gulf, this thing drains regularly (6) 26. Reports of demise of spheres of influence once king gone (5) 27. Perhaps one of the rulers of Mecca has border with it finally secure (9) 28. Die singly, carved up into concessions (9) 29. Coming from the East, hidden by mask, ruthless former rulers of 4 down? (5) Down: 1. Where you'll find dockworkers are out for major conflict over hours (5) 2. They take the blame as I step out of line (7) 3. Somewhere around north of 4 down say? (5) 4. Where first known poet wrote alternating quatrain with turnaround (4) 5. Loose match arranged with fellow and student (10) 6. see 11 across 7. In Cannes, utter rejection of superficial elements in 2021 French musical (7) 8. Europe initially put hold on rates for movement between banks (9) 13. Make resilient and set free, and get shoe and boot every other one out of there! (10) 14. Compulsory corporate consolidation conservative? (9) 15. Badly divided oil spread trouble (9) 18. Rob pride, shattered by Allied victory (7) 20. More worried as river getting turbulent past corner (7) 23. Entire but not entire, moving but not moving (5) 24. Ends of this scheme were touted as beginnings (5) 25. Terror group is doubled (4)


@(item: model.MetaData, curlPaths: Map[String, String] = Map())(implicit request: RequestHeader)
@import conf.Switches._
@import conf.Static
@import conf.Configuration
@* Critical scripts that should execute as soon as possible, preventing layout thrashing *@


The main difference between a Hit area checkbox and a basic one is the styling and the fact that it's clickable on its full width.
Since it should be used in a list context, the hit-area-box
class can only be applied on an ul
.
Just like in the checkbox version, the Hit area radio button needs to be used on an ul
.


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Gurgaon, Haryana, 122018
P: +91<PHONE_NUMBER>
8th Floor, A wing, Reliable
Tech Park, Behind Reliable
Plaza, Off Thane-Belapur Road,
Airoli, Navi Mumbai, 400708
P: +91 22 6687 4707
1st & 2nd Floor IBIS Hotel 26/1
Hosur Road, Bangalore
560068
P: +91 80 67027161
Level 2, KLJ Tower North
B5 District Centre
Netaji Subash Place
New Delhi, 110034
P: +91 11 66481080


With WAMP Live Data Widgets, it's easy to implement a live display of your data in the browser.
You can combine the widgets listed below to create dashboards, or integrate one or more into your Web pages as needed.
Sample Dashboard Controller for the DashboardTo test the widgets, open the dashboard and controller on separate displays or devices. Changes entered in the controller are displayed on the dashboard in real time.
The following widgets are available:


Playing with Notes Client form layout
In a recent training session a participant asked why Notes forms are so ugly. Naturally I pointed him to Chris. Nevertheless he got me thinking. How many steps does it take to make a Notes form looking nice. Let us start with a very typical basic form:
The first thing will be the background for toolbar and form. I borrow some style from Lotus Connections: adding background images leads here:
Next step is to take care of the radio buttons, mandatory fields and the borders.The result is not too bad.
Now I fill the background of the cells and switch off the field delimiters.
As a final step replace the entry helpers with hyperlinks and format the title. I also played with a different button style.
As you can see: it takes only a few steps to make good looking forms in the Notes client.
Update: Jane asked how I replaced the entry helpers with hyperlinks. It is a two step process:
First remove the display of the entry helper in the field properties:
Then add create a new action hotspot (format it blue underline for consistency with Internet standards) and add the appropriate @Prompt function. The hotspot action for region looks like this:
FIELD Region := @Prompt([OkCancelList]:[NoSort];"Select Keywords";"Keywords";Region;"North":"Central":"South")
Please not that you should use "Select Keywords" and "Keywords" for your prompt, so the UI is consistent between mouse clickers and keyboard hitters. Also it is a good idea to use a hidden ComputedForDisplay field to have the keyword field and the prompt draw the same values.
The first thing will be the background for toolbar and form. I borrow some style from Lotus Connections: adding background images leads here:
Next step is to take care of the radio buttons, mandatory fields and the borders.The result is not too bad.
Now I fill the background of the cells and switch off the field delimiters.
As a final step replace the entry helpers with hyperlinks and format the title. I also played with a different button style.
As you can see: it takes only a few steps to make good looking forms in the Notes client.
Update: Jane asked how I replaced the entry helpers with hyperlinks. It is a two step process:
First remove the display of the entry helper in the field properties:
Then add create a new action hotspot (format it blue underline for consistency with Internet standards) and add the appropriate @Prompt function. The hotspot action for region looks like this:
FIELD Region := @Prompt([OkCancelList]:[NoSort];"Select Keywords";"Keywords";Region;"North":"Central":"South")
Please not that you should use "Select Keywords" and "Keywords" for your prompt, so the UI is consistent between mouse clickers and keyboard hitters. Also it is a good idea to use a hidden ComputedForDisplay field to have the keyword field and the prompt draw the same values.
Posted by Stephan H Wissel on 14 November 2007 | Comments (7) | categories: Show-N-Tell Thursday


Last update on March 20, 2023
Research areas: EDA, Routability, Detailed Placement, Artificial Intelligence and High Performance Computing.
As member of Clock Concurrent Optimization (CCOpt) team
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Mehn! 2018 has been kind to me. I have enjoyed God's goodness. But I have learned a lot as well. For one, I have learnt not to have a fixed mindset. That is limit my understanding of myself. Indeed, there is nothing I cannot learn how to do well with time and practise.
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library(phackR)
In the literature, different papers describe different p-hacking strategies. We assemble the p-hacking strategies that were mentioned by other authors and provide an R-package to simulate their effects on the results of analyses. Here, we describe the p-hacking strategies and the simulation functions in detail.
Every p-hacking strategy is stored in a separate file. In each file,
there are three main code blocks. The first block contains a function to
simulate data that can be used with the p-hacking strategy (or a link to
a more generic function that can be used, e.g., from the “helpers.R”
file). The second block contains the p-hacking function. Here, we define
the strategy to analyze the data with (different levels of) p-hacking.
Typically, these functions take the data that were created in the first
block as an input. The output is a list with two elements:
p.final
, i.e., the final p-hacked p-value, and
ps
, i.e., all p-values “visited” in the process. Typically
the first ps value is the original, non-p-hacked p-value. The third
block combines the functions from the first two code blocks in one
simulation function. Essentially, the functions in this block consist of
a big loop where data are created and analyzed repeatedly. The output of
this function is always a matrix with two columns: The first column
contains the p-hacked values, i.e., the p.final
values from
the p-hacking function. The second column contains the “raw” non
p-hacked values. If the p-hacking was “successful” in the respective
iteration, the first column will show a smaller p-value than the second
column. If the p-hacking was “unsuccessful”, the first and second column
won’t differ.
Data simulation functions, p-hacking functions, and any helper
functions are specified as internal functions, i.e., start with a “.”
and are not exported (alas, we don’t want people to use this as a
toolkit for p-hacking, so let’s use this as a safety belt…). All
simulation functions are exported and start with “sim.”. All functions
are documented using roxygen
documentation. Unit tests of
the simulation functions can be found in the tests
folder.
We use two kinds of hypothesis tests: the independent samples t-test and univariate linear regression. The standard choice is the independent samples t-test. However, some p-hacking methods are not easily applicable to this hypothesis test or are more likely to be used in a continuous variable case. In these cases, we use a univariate linear regression. Some p-hacking strategies specifically rely on different hypothesis tests. In these cases, this is mentioned in the description of the p-hacking strategies below.
The result object of the p-hacking simulation functions contains the original and p-hacked p-value for all analyses. Additionally, whenever possible the determination coefficient \(R^2\) of the original and p-hacked result is shown as well. When only t-tests are used in the simulation, the output also contains the original and p-hacked Cohen’s d. The “hacked” effect sizes are always based on the p-hacked p-values, i.e., the analysis is still selected based on the smallest p-value, not on the smallest effect size.
Most p-hacking simulation functions have an argument “strategy”. This
argument determines how the final p-value is chosen. There are three
options: strategy = "firstsig"
simulates the situation
where a researcher tries out some p-hacking methods and stops as soon as
the result is significant, that is, at the first significant p-value. In
a comment on Simonsohn et al. (2014), Ulrich & Miller (2015) argued
that researchers might instead engage in “ambitious” p-hacking, where
the smallest significant p-value is selected. This strategy is
implemented in the option strategy = "smallest.sig"
.
Simonsohn (private comm.) argues that there might exist a third
p-hacking strategy where the researchers try a number of different
analysis options and select the smallest p-value no matter if it is
significant or not. We implemented this in the option
strategy = "smallest"
. This strategy implies that
researchers use p-values as a criterion for validity of analyses
“whichever analysis works better is probably the right analysis to keep”
(Simonsohn, private comm.).
The true effect size in the simulations is always zero. This means that the data are simulated such that there is no difference between the means of the two groups in the t-test (mean = 0, sd = 1) or that the correlation between the two variables of interest is zero.
All p-hacking simulation functions come with the same default settings for common arguments. We will only explain these once and mention below if anything differs from these defaults.
strategy
: As described above, this argument defines the
strategy of selecting p-values (strategy = "firstsig"
),
(strategy = "smallest.sig"
), or
(strategy = "smallest"
). The default setting is
strategy = "firstsig"
.alpha
: The significance level of the hypothesis test.
If p < alpha, the result is deemed significant. The default setting
is alpha = 0.05
.iter
: Number of iterations in the simulation process.
The default is iter = 1000
, i.e., the simulation will
result in 1000 p-hacked p-values.alternative
: Whenever a t-test is used as an analysis
function, the argument alternative
specifies the direction
of the t-test. As in the stats::t.test
function, the
default is two.sided
.We simulate this p-hacking strategy using the t-test. The
simulated dataset contains one discrete group variable
with two levels (independent variable) and several continuous variables
(dependent variables). The correlation between the dependent variables
can be set using the argument r
. The size of the groups can
be defined by nobs.group
(either an integer for equally
sized groups or a vector with two elements for groups of different
sizes). The p-hacking strategy is implemented as
follows: Compute t-test with every single of the dependent variables.
Then select the final p-value using one of the p-value selection
strategies (first significant p-value, smallest significant p-value,
smallest p-value). The output contains the original and p-hacked
p-values, the original and p-hacked \(R^2\), and the original and p-hacked
Cohen’s d.
The simulation code can be found in the file
selectiveReportingDV.R
.
Example
set.seed(1234)
sim.multDVhack(nobs.group = 30, nvar = 5, r = 0.3, strategy = "smallest",
iter = 10, alternative = "two.sided", alpha = 0.05)
#> ps.hack ps.orig r2s.hack r2s.orig ds.hack ds.orig
#> [1,] 0.06164974 0.7702692 0.05892560 0.0014819268 -0.4920490 -0.07575369
#> [2,] 0.08867750 0.7649193 0.04915105 0.0015538141 0.4470736 0.07757210
#> [3,] 0.18669108 0.2803161 0.02986554 0.0200654432 0.3450147 -0.28138064
#> [4,] 0.11127663 0.9113189 0.04314289 0.0002156974 -0.4175413 0.02888269
#> [5,] 0.13022740 0.6269691 0.03903742 0.0040991187 -0.3963289 0.12615532
#> [6,] 0.06986678 0.1830474 0.05554370 0.0303575006 -0.4768643 -0.34793294
#> [7,] 0.07291094 0.3168725 0.05439489 0.0172710516 0.4716203 0.26068190
#> [8,] 0.27619604 0.8442438 0.02040887 0.0006710488 -0.2838281 -0.05095552
#> [9,] 0.16142965 0.1614296 0.03352364 0.0335236356 -0.3662253 -0.36622527
#> [10,] 0.14650802 0.4550353 0.03599870 0.0096583411 -0.3799907 -0.19419011
This p-hacking strategy can be simulated using a t-test or a
regression analysis with the provided function. For the t-test, the
simulated dataset contains one control group variable
and multiple treatment group variables (independent variables), thus it
is in a wide format. The correlation between the treatment group
variables can be set using the argument r
. The size of the
groups can be defined by nobs.group
. Note that unequal
sizes of groups are not possible here because the data set is in wide
format. For the regression analysis, the simulated dataset contains one
criterion (dependent) variable, and multiple predictor (independent)
variables. The p-hacking strategy is implemented as
follows for the t-test: Conduct an independent samples t-test with every
single treatment variable and the control group variable. Then select
the final p-value using one of the p-value selection strategies (first
significant p-value, smallest significant p-value, smallest p-value).
For the regression analysis, the test is conducted with each predictor,
then the final p-value is selected using one of the p-value selection
strategies. The output contains the original and p-hacked p-values, the
original and p-hacked \(R^2\), and the
original and p-hacked Cohen’s d.
The simulation code can be found in the file
selectiveReportingIV.R
.
Example
set.seed(1234)
sim.multIVhack(nobs.group = 30, nvar = 5, r = 0.3, strategy = "smallest",
regression = FALSE, iter = 10, alternative = "two.sided",
alpha = 0.05)
#> ps.hack ps.orig r2s.hack r2s.orig ds.hack ds.orig
#> [1,] 0.03656040 0.03656040 0.073189717 0.0731897168 -0.5525836 -0.55258358
#> [2,] 0.61999994 0.67965745 0.004266656 0.0029609101 0.1287184 0.10715807
#> [3,] 0.48553332 0.72651137 0.008423249 0.0021252894 0.1812362 -0.09074849
#> [4,] 0.34547020 0.72990489 0.015358790 0.0020707973 -0.2455884 -0.08957510
#> [5,] 0.02942400 0.91252899 0.079163273 0.0002098281 0.5765526 -0.02848694
#> [6,] 0.08267945 0.08267945 0.051021072 0.0510210724 -0.4559475 -0.45594754
#> [7,] 0.27266052 0.46496393 0.020708644 0.0092421584 0.2859488 0.18992027
#> [8,] 0.66678887 0.66678887 0.003218702 0.0032187017 0.1117400 0.11174003
#> [9,] 0.17456156 0.80477693 0.031547552 0.0010618789 0.3549049 0.06411159
#> [10,] 0.24943695 0.41883769 0.022803495 0.0113016364 0.3003849 -0.21023602
We simulate this p-hacking strategy using the t-test, but literally
any hypothesis test should produce the same results. The
simulated dataset contains one discrete group variable
with two levels (independent variable) and one continous variable
(dependent variable). Note that the simulation function does not include
an argument for the group size as the group size is irrelevant in this
p-hacking context. The p-hacking strategy is
implemented as follows: Compute the p-value for the t-test, if it is
between the alpha level and a rounding level specified in the argument
roundinglevel
(e.g., 0.06 > p > 0.05), round it down
to p = 0.05, else keep the original p-value. Note that selecting a
p-value selection strategy (first significant p-value, smallest
significant p-value, smallest p-value) is not possible for this
p-hacking strategy. The output contains the original and p-hacked
p-values, and the original and p-hacked \(R^2\). Note that \(R^2\) does not change because it is
unlikely that researchers would adjust effect sizes retrospectively
based on a rounded p-value.
The simulation code can be found in the file
incorrectRounding.R
.
Example
set.seed(1234)
sim.roundhack(roundinglevel = 0.06, iter = 10, alternative = "two.sided",
alpha = 0.05)
#> ps.hack ps.orig r2s.hack r2s.orig
#> [1,] 0.2930249 0.2930249 1.904439e-02 1.904439e-02
#> [2,] 0.9074418 0.9074418 2.350552e-04 2.350552e-04
#> [3,] 0.9470891 0.9470891 7.658541e-05 7.658541e-05
#> [4,] 0.8114894 0.8114894 9.887708e-04 9.887708e-04
#> [5,] 0.8986408 0.8986408 2.821287e-04 2.821287e-04
#> [6,] 0.8353421 0.8353421 7.510586e-04 7.510586e-04
#> [7,] 0.4623168 0.4623168 9.351747e-03 9.351747e-03
#> [8,] 0.7801766 0.7801766 1.353649e-03 1.353649e-03
#> [9,] 0.4914814 0.4914814 8.196844e-03 8.196844e-03
#> [10,] 0.8113854 0.8113854 9.898828e-04 9.898828e-04
We simulate this p-hacking strategy using the t-test. The
simulated dataset contains one discrete group variable
with two levels (independent variable) and one continous variable
(dependent variable), both should have at least the sample size defined
in n.max
. Note that this is the reason why the simulation
function does not have an argument to specify the sample size per group
(it is automatically set to n.max). The p-hacking
strategy is implemented as follows: The dataset is evaluated
row-by-row, starting with a minimum sample size of n.min
.
At each step, a number of observations is added to the sample, defined
by the argument step
and the t-test is computed. This
continues until the maximum sample size specified in n.max
is reached. The p-hacked p-value is defined as the first p-value that is
smaller than the defined alpha level. The non-p-hacked p-value is
defined as the p-value at n.max (this implies that if there was no
p-hacking, n.max would be the specified fixed sample size). The output
contains the original and p-hacked p-values, the original and p-hacked
\(R^2\), and the original and p-hacked
Cohen’s d.
The simulation code can be found in the file
optionalStopping.R
.
Example
set.seed(1234)
sim.optstop(n.min = 10, n.max = 20, step = 2, alternative = "two.sided",
iter = 10, alpha = 0.05)
#> ps.hack ps.orig r2s.hack r2s.orig ds.hack ds.orig
#> [1,] 0.26352442 0.26352442 3.278363e-02 3.278363e-02 0.35888764 0.35888764
#> [2,] 0.01202975 0.03081077 3.022653e-01 1.169126e-01 -1.24882102 -0.70928483
#> [3,] 0.21285027 0.21285027 4.053487e-02 4.053487e-02 0.40067441 0.40067441
#> [4,] 0.27429670 0.27429670 3.136386e-02 3.136386e-02 -0.35077307 -0.35077307
#> [5,] 0.53201656 0.53201656 1.035928e-02 1.035928e-02 0.19944254 0.19944254
#> [6,] 0.79232864 0.79232864 1.846673e-03 1.846673e-03 0.08384712 0.08384712
#> [7,] 0.93976306 0.93976306 1.522541e-04 1.522541e-04 0.02405522 0.02405522
#> [8,] 0.43862992 0.43862992 1.586743e-02 1.586743e-02 -0.24752458 -0.24752458
#> [9,] 0.96766599 0.96766599 4.381244e-05 4.381244e-05 -0.01290328 -0.01290328
#> [10,] 0.68865560 0.68865560 4.271639e-03 4.271639e-03 0.12767876 0.12767876
We simulate this p-hacking strategy using univariate linear
regression (most outlier detection methods are based on some kind of
distance measures on continuous variables). The simulated
dataset contains two continuous variables, both with the sample
size specified in the argument nobs
. The p-hacking
strategy is implemented as follows: The original p-value is
computed as a simple linear regression(y ~ x) with all values. For the
p-hacked p-values, outlier values are searched in x and y using an
outlier definition criterion. Then, a pairwise deletion of outlier
values follows for the following three options: (1) remove xy pairs
where x is an outlier, (2) remove xy pairs where y is an outlier, (3)
remove xy pairs where x and y are outliers. Therefore, for every outlier
deletion strategy, we end up with three datasets where different values
are removed (some of these may be identical and will be cut away by the
.extractoutlier
function in practice). The p-hacking method
contains 12 separate outlier definition criteria, some of which have
several sub-criteria (e.g., delete values that are > x
standard deviations above or below the mean, with x being 2,
2.5, 3, …). In the case of sub-criteria, the outlier deletion process is
applied to each sub-criterion, so that we end up with 3 x number of
sub-criteria data sets (again, some of which might be identical and
cut away by the .extractoutlier
function). In the
simulation function, either all separate outlier definition criteria can
be applied (i.e., which = c(1:12)
), a specific subset can
be chosen (e.g., which = c(1,3,5)
), or a random subset of 5
different outlier strategies can be chosen
(which = "random"
). We believe that the latter shows a
realistic scenario as not all researchers know all outlier definition
criteria and the ones they know are probably a random subset of the
existing outlier definition criteria (which does not mean that our
simulation method covers all existing outlier definition criteria). For
all outlier definition criteria, the outlier-deleted datasets are
created and evaluated using the t-test function. In the end, the final
p-value is selected using one of the p-value selection strategies (first
significant p-value, smallest significant p-value, smallest p-value).
The output contains the original and p-hacked p-values as well as the
original and p-hacked \(R^2\).
Below, we describe all outlier exclusion criteria:
graphics::boxplot
function.aplpack::stemleaf
function.stats::influence.measures
).The simulation code can be found in the file
outlierExclusion.R
.
Example
set.seed(1234)
sim.outHack(nobs = 30, which = "random", strategy = "smallest", alpha = 0.05,
iter = 10)
#> ps.hack ps.orig r2s.hack r2s.orig
#> [1,] 0.36575765 0.5701855 0.03156281 0.0116520628
#> [2,] 0.23919897 0.4829183 0.06237719 0.0177379336
#> [3,] 0.44444558 0.7770139 0.02358010 0.0029119601
#> [4,] 0.11634926 0.2228680 0.08882336 0.0525830930
#> [5,] 0.36854013 0.6887060 0.03245621 0.0058179850
#> [6,] 0.09832312 0.6882591 0.11929196 0.0058355770
#> [7,] 0.19847431 0.9127607 0.06276985 0.0004362954
#> [8,] 0.36213570 0.3621357 0.02974142 0.0297414168
#> [9,] 0.35302939 0.6725462 0.03759783 0.0064737516
#> [10,] 0.02308360 0.4682688 0.18988823 0.0189439383
We simulate this p-hacking strategy using a t-test that is expanded
to an ANCOVA when covariates are taken into account. The
simulated dataset contains a discrete group variable
with two levels (independent variable), a continuous dependent variable,
and a set of continuous covariates. The number of covariates can be
specified using the argument ncov
. The correlation of the
dependent variable with the covariates as well as the correlation among
the covariates can be specified using the arguments rcovdv
and rcov
, respectively. The p-hacking
strategy is implemented as follows: First, the original p-value
without covariates is calculated. Then, all covariates are added to the
model separately (i.e., dv ~ group + cov1, df ~ group + cov2, …). Then,
covariates are ordered in decreasing order with respect to their
correlation with the dependent variable and then added to the model
sequentially (i.e., dv ~ group + covhighest, dv ~ covhighest +
covsecondhighest, …). All models are evaluated and the final p-value is
selected using one of the strategies (first significant p-value,
smallest significant p-value, smallest p-value). The models can either
be defined as models without interaction terms
(interactions = FALSE
as is the default) or with
interaction terms. The output contains the original and p-hacked
p-values as well as the $ partial,^2$. In this case, we do not display
the overall \(R^2\) of the analyses
because in the ANCOVAs the focus does not lie on the overall explained
variance.
The simulation code can be found in the file
exploitCovariates.R
.
Example
set.seed(1234)
sim.covhack(nobs.group = 30, ncov = 4, rcov = 0.3, rcovdv = 0.5,
interactions = FALSE, strategy = "smallest",
alpha = 0.05, iter = 10)
#> ps.hack ps.orig eta2s.hack eta2s.orig
#> [1,] 0.47759857 0.7702692 0.009382178 0.0014819268
#> [2,] 0.26101986 0.7649193 0.022912587 0.0015538141
#> [3,] 0.08942181 0.2803161 0.049772287 0.0200654432
#> [4,] 0.23833451 0.9113189 0.024307457 0.0002156974
#> [5,] 0.30059661 0.6269691 0.018785707 0.0040991187
#> [6,] 0.03237891 0.1830474 0.077828657 0.0303575006
#> [7,] 0.07969574 0.3168725 0.052900736 0.0172710516
#> [8,] 0.42125010 0.8442438 0.012016761 0.0006710488
#> [9,] 0.11788297 0.1614296 0.042352235 0.0335236356
#> [10,] 0.15415091 0.4550353 0.035300865 0.0096583411
We simulate this p-hacking strategy using a t-test. The
simulated dataset consists of one discrete group
variable (independent variable), one continuous dependent variable, and
several binary subgroup variables. The size of the original groups can
be defined by nobs.group
(either an integer for equally
sized groups or a vector with two elements for groups of different
sizes). Group sizes in the subgroup variables can differ (group
membership is sampled randomly from c(0,1)
). The number of
subgroup variables can be defined using the argument
nsubvars
. The p-hacking strategy is
implemented as follows: First, the original p-value is computed using
the full dataset (independent and dependent variable). Then, for each
subgroup variable, the dataset is split into two parts and the t-test is
conducted separately in each part (e.g., as if one would conduct the
same t-test for men and women). Then the final p-value is selected using
one of the p-value selection strategies (first significant p-value,
smallest significant p-value, smallest p-value). The output contains the
original and p-hacked p-values, the original and p-hacked \(R^2\), and the original and p-hacked
Cohen’s d.
Note: This p-hacking strategy can both be seen as a subgroup analysis (e.g., “evaluate data separately for men and women”) or as tinkering with inclusion criteria (e.g., “include only women (if the analysis is significant for them)”). Technically, for tinkering with inclusion criteria, maybe only one of the levels of the subgroups should be evaluated but this would imply that the selection of the inclusion criterion was theory-driven. The current approach does not assume this (inclusion criterion is “HARKed”).
The simulation code can be found in the file
subgroupAnalysis.R
.
Example
set.seed(1234)
sim.subgroupHack(nobs.group = 30, nsubvars = 3, alternative = "two.sided",
strategy = "smallest", alpha = 0.05, iter = 10)
#> ps.hack ps.orig r2s.hack r2s.orig ds.hack ds.orig
#> [1,] 0.27466289 0.29302489 0.03836236 1.904439e-02 0.3873471 0.27398526
#> [2,] 0.12310738 0.95057443 0.07740683 6.681601e-05 -0.5620178 -0.01607397
#> [3,] 0.09744909 0.83534209 0.11987580 7.510586e-04 0.7091575 0.05390989
#> [4,] 0.05376876 0.38322021 0.11478620 1.313575e-02 -0.7009333 -0.22686477
#> [5,] 0.07008478 0.38793977 0.14145828 1.287904e-02 0.7772652 0.22460781
#> [6,] 0.01059256 0.28750383 0.24253362 1.948104e-02 1.0905409 0.27717017
#> [7,] 0.14729545 0.73205279 0.09732184 2.036715e-03 -0.6280949 -0.08883338
#> [8,] 0.01541254 0.09297363 0.21289308 4.789192e-02 -1.0184963 -0.44101804
#> [9,] 0.25867761 0.40920899 0.04532332 1.177476e-02 0.4247919 0.21464282
#> [10,] 0.34139023 0.34139023 0.01561864 1.561864e-02 0.2476899 0.24768991
We simulate this p-hacking strategy using univariate linear
regression (so that we don’t mix it up with the multiple dependent
variable p-hacking strategy). The simulated dataset
consists of one dependent variable and several correlated score
variables that can potentially form the items of a score (these are not
correlated with the dependent variable). The correlation between these
score variables can be specified using the rcomp
argument,
the number of the score variables can be specified using the
ncompv
argument. The p-hacking strategy is
implemented as follows: First, the original p-value is calculated using
the mean score of all score variables as a predictor variable. Then, one
by one, items are removed from the scale. The next item to remove is
always chosen based on “Cronbach’s alpha when item deleted” (the common
choice in SPSS), i.e., the item is chosen that makes the scale most
consistent. In each iteration, the p-values for linear regressions using
the following predictors are calculated: (1) new composite score, i.e.,
reduced item scale; (2) deleted item from the score. The maximum number
of items to be removed from the scale can be set using the argument
ndelete
. In the end, the final p-value is selected using
one of the p-value selection strategies (first significant p-value,
smallest significant p-value, smallest p-value). The output contains the
original and p-hacked p-values as well as the original and p-hacked
\(R^2\).
The simulation code can be found in the file
compositeScores.R
.
Example
set.seed(1234)
sim.compscoreHack(nobs = 30, ncompv = 5, rcomp = 0.7, ndelete = 3,
strategy = "smallest", alpha = 0.05, iter = 10)
#> ps.hack ps.orig r2s.hack r2s.orig
#> [1,] 0.5645814 0.7554491 0.01199198 3.520781e-03
#> [2,] 0.3409324 0.5021432 0.03243425 1.624187e-02
#> [3,] 0.5479496 0.6513339 0.01303920 7.397467e-03
#> [4,] 0.1403261 0.2543529 0.07600244 4.613560e-02
#> [5,] 0.3767909 0.9976487 0.02799992 3.157301e-07
#> [6,] 0.1380544 0.2094160 0.07684615 5.566566e-02
#> [7,] 0.4650607 0.7333174 0.01921601 4.211740e-03
#> [8,] 0.3057457 0.3729319 0.03741765 2.844951e-02
#> [9,] 0.2326683 0.3390684 0.05046840 3.268144e-02
#> [10,] 0.3308616 0.4481106 0.03379106 2.070317e-02
We simulate this p-hacking strategy using univariate linear
regression because most variable transformation strategies are not
applicable to discrete variables. The simulated dataset
contains two continuous variables that are not correlated with each
other. The p-hacking strategy is implemented as
follows: The original p-value is calculated as a simple univariate
linear regression y ~ x. Then, the variables in the model are
transformed. Using the argument transvar
one can specify
whether only x (transvar = "x"
), only y
(transvar = "y"
) or both (transvar = "xy"
)
should be transformed. The respective variables are transformed using a
log transformation, a square root transformation and the inverse. The
function offers the option that variables are only transformed
conditional on a significant normality test of residuals (Shapiro-Wilk).
This can be adjusted using the argument testnorm
. Once the
variables have been transformed, linear regressions are computed for all
transformations of the variables. The final p-value is selected using
one of the p-value selection strategies (first significant p-value,
smallest significant p-value, smallest p-value). The output contains the
original and p-hacked p-values as well as the original and p-hacked
\(R^2\).
The simulation code can be found in the file
variableTransformation.R
.
Example
set.seed(1234)
sim.varTransHack(nobs = 30, transvar = "xy", strategy = "smallest",
alpha = 0.05, iter = 10)
#> ps.hack ps.orig r2s.hack r2s.orig
#> [1,] 0.17797866 0.5701855 0.06383310 0.0116520628
#> [2,] 0.16510978 0.4829183 0.06764924 0.0177379336
#> [3,] 0.05056903 0.7770139 0.12972626 0.0029119601
#> [4,] 0.05253812 0.2228680 0.12770161 0.0525830930
#> [5,] 0.31893767 0.6887060 0.03546773 0.0058179850
#> [6,] 0.32296302 0.6882591 0.03489290 0.0058355770
#> [7,] 0.04594833 0.9127607 0.13480334 0.0004362954
#> [8,] 0.11948981 0.3621357 0.08434808 0.0297414168
#> [9,] 0.06834996 0.6725462 0.11375829 0.0064737516
#> [10,] 0.22190875 0.4682688 0.05279580 0.0189439383
We simulate this p-hacking strategy using a t-test / ANOVA (but the underlying data is actually continuous). The simulated dataset contains two continuous variables that are uncorrelated (dependent and independent variable). In the p-hacking strategy, three mechanisms are applied to make the independent variable discrete. The first mechanism is a median split (leading to two groups, ergo a t-test). The second mechanism is a three-wise split, after which only the two extreme categories are compared in a t-test (“cut the middle” strategy). The third mechanism is a three-wise split again, but this time all three categories are evaluated using an ANOVA (only the omnibus test is evaluated). Additionally to these, the original univariate linear regression is evaluated to obtain the original p-value. Then the final p-value is selected using one of the p-value selection strategies (first significant p-value, smallest significant p-value, smallest p-value). The output contains the original and p-hacked p-values as well as the original and p-hacked \(R^2\).
The simulation code can be found in the file
exploitCutoffs.R
.
Example
set.seed(1234)
sim.cutoffHack(nobs = 30, strategy = "smallest", alpha = 0.05, iter = 10)
#> ps.hack ps.orig r2s.hack r2s.orig
#> [1,] 0.3607383 0.5701855 0.04658767 0.0116520628
#> [2,] 0.2150510 0.4829183 0.10760193 0.0177379336
#> [3,] 0.3774882 0.7770139 0.04350891 0.0029119601
#> [4,] 0.1553856 0.2228680 0.07075440 0.0525830930
#> [5,] 0.4054100 0.6887060 0.02485252 0.0058179850
#> [6,] 0.4326364 0.6882591 0.06017682 0.0058355770
#> [7,] 0.4908072 0.9127607 0.01711245 0.0004362954
#> [8,] 0.1438563 0.3621357 0.11483785 0.0297414168
#> [9,] 0.1406796 0.6725462 0.13521925 0.0064737516
#> [10,] 0.2629142 0.4682688 0.06907680 0.0189439383
We simulate this p-hacking strategy using a t-test (and related tests for comparing the central tendency in two independent groups). The simulated dataset contains a discrete group variable (independent variable) and a continuous dependent variable. The p-hacking strategy is implemented as follows: The central tendency in the groups is compared using a t-test (original analysis), a Welch test, a Wilcoxon test, and a Yuen test (trimming options: 0.1, 0.15, 0.2, and 0.25). Then the final p-value is selected using one of the p-value selection strategies (first significant p-value, smallest significant p-value, smallest p-value). The output contains only the original and p-hacked p-values because there is no common effect size for the analayses used.
The simulation code can be found in the file
statAnalysis.R
.
Example
set.seed(1234)
sim.statAnalysisHack(nobs.group = 30, strategy = "smallest",
alternative = "two.sided", alpha = 0.05, iter = 10)
#> ps.hack ps.orig
#> [1,] 0.08380534 0.2930249
#> [2,] 0.79732252 0.9074418
#> [3,] 0.43098291 0.9470891
#> [4,] 0.26377839 0.8114894
#> [5,] 0.62983299 0.8986408
#> [6,] 0.71541493 0.8353421
#> [7,] 0.46231685 0.4623168
#> [8,] 0.78017663 0.7801766
#> [9,] 0.44043071 0.4914814
#> [10,] 0.81138541 0.8113854
We simulate this p-hacking strategy using univariate linear
regression because many imputation methods have been developed for
continuous variables. The simulated dataset contains
two non-correlated continuous variables. Both of these contain missing
values. The percentage of missing values is defined by the argument
missing
. The p-hacking strategy is
implemented similar to the outlier exclusion strategy. There are 10
different imputation methods that can be selected using the argument
which
. It is possible to select all of them
(which = c(1:10)
), a specific subset of them (e.g.,
which = c(1,4,6)
), or a random subset of 5 imputation
methods (which = "random"
). In the end, the final p-value
is selected using one of the p-value selection strategies (first
significant p-value, smallest significant p-value, smallest p-value).
The output contains the original and p-hacked p-values as well as the
original and p-hacked \(R^2\).
Below, we describe all imputation methods:
mice
R-package.mice
R-package.mice
R-package.mice
R-package.mice
R-package.mice
R-package.The simulation code can be found in the file
favorableImputation.R
.
Example
set.seed(1234)
sim.impHack(nobs = 30, missing = 0.2, which = c(1:10), strategy = "smallest",
alpha = 0.05, iter = 10)
#> ps.hack ps.orig r2s.hack r2s.orig
#> [1,] 0.276458363 0.69277353 0.04214592 0.008876505
#> [2,] 0.145723679 0.65682616 0.07405483 0.012647900
#> [3,] 0.002853465 0.02624177 0.27630402 0.258465026
#> [4,] 0.307547019 0.94202988 0.03714524 0.000302004
#> [5,] 0.022233604 0.41897902 0.17304224 0.036618614
#> [6,] 0.187111897 0.69119635 0.06130433 0.009513082
#> [7,] 0.110755379 0.56048920 0.08831379 0.019168437
#> [8,] 0.161107126 0.81745840 0.06890273 0.003222120
#> [9,] 0.069976774 0.72774996 0.11251271 0.006898171
#> [10,] 0.069546669 0.36138015 0.11283916 0.044013030


visionary youth group mentors during a mentorship programme at kiandutu primary school
We champion for a generation free of alcohol taking,smoking and other drugs by teaching the community the effects of drug addiction to our health and wellbeing.We visit,guide and encourage people in rehabilitation centers
Visionary youth group have engaged in several environment conservation activities like tree planting and cleaning within Murang'a town and its outskirts.We acknowledge the impact of a clean environment to the better future and our role in ensuring this
our members offer their skills and academic abilities to motivate the young generation in primary and highschools.we even go ahead and offer free tuition to students during their free time.
our concern is the well being of the young generation,not just academically but also physically encouraging talent development.


A: Head over to your profile page. The fields there are dynamic, make any changes and press submit.
A: The method of payment is entirely up to the seller and the buyer.
A: Users are required to include either their email address or phone number. At least one of these forms of contact will be available at the bottom of the seller's listing.


How do I compile and run the examples? For instructions, refer to The "Hello World!" Application lesson in the Getting Started trail. This lesson includes links to download the Java SE Development Kit 6 (also known as JDK™ 6) for Windows, Linux, Solaris, and Mac OS X. There are also instructions and links for the NetBeans™ IDE, which runs on all the above platforms.
Why do I get an error message when I try to compile or run an example? Check out Getting Started.
I already have the JRE. Why do I need the JDK? The Java SE Runtime Environment, or JRE, allows you to run programs that have already been compiled and deployed. We hope you will use the Tutorials to expand your understanding of Java technology by modifying and extending the examples. To do this, you need the JDK.
I'm just not ready to upgrade to JDK 6. Can I get the old Tutorials? You can get the old tutorials from the Java Technology Download page. But note that a lot of material in the old Tutorials is very out of date, even for version 5.0 of the platform. You can actually use the new Tutorials with JDK 5.0; just recompile the examples. Most of the current examples will recompile without change; the rest will recompile with minor changes.
Why doesn't Sun provide software for Mac OS X?. Java platform software for Macintosh OS X is maintained by Apple Computer; the OS X version of JDK 5.0 is included with the OS. Apple also provides JDK 6 as Beta software; to download it, go to connect.apple.com. For more information about the Java platform on Mac OS X, go to developer.apple.com/java.


Contents
- Tiberius
- Paranoid, false accused people --> Lower reputation
- Caligula
- Wanted to make Incitatus (horse) --> Consul
- Claudius
- A good scholar, too young to handle pressure
Nero:
-
No respect for Senate
-
Cruel and insane
-
Killed Wife and Mother
-
Persecuted Jews
-
Committed suicide because knew he would be murder
Emperors from Flavian Dynasty
-
Vespasian (69-79)
-
Titus (79-81)
-
Domitian (81-96)
5 Best
-
Nerva (96-98)
-
Trajan (98-117)
-
Hadrian (117-138)
-
Antonius Pius (138-161)
-
Marcus Aurelius (161-180)
20-25 emperors in 49 years
-
Frontiers weaken:
-
Empire split into 3 parts:
This caused Claudias Gothicus and Aurelian (Empire Generals) --> Come in and stablized
Later Diocletioan (284-305) created Autocracy.
- Autocracy
- No Senate, Emperor had absolute power.
This brought 2 centuries to Roman Empire
-
Constantine (306-337 A.D.) legalized Christianity in 313 A.D.
-
Theodosius (379-395 A.D.) --> Christianity offical 391 A.D
-
East: Orthodox Church:
-
Capital: Constantinople
-
Leader: Patriach
-
West: Roman Catholic:
-
Capital: Rome
-
Leader: Pope
They both helped each other, but had totally different viewpoints.
Monguls invaded --> West Empire
Romulus Augustulus deposed --> 476 A.D.
However, the East Survived (Byzantine Empire) until 1453 A.D.
Orignally --> Rome was tolerant
-
Incorporated other religions
-
Tolerated Jews (Refused to convert)
Rome wants to maintain:
-
Order (pietas)
-
Favor for Rome
Christianity was a threat to both.
-
Started as a sect
-
Some were extremists --> Out right refused other religions
-
Reliable --> Keep eyes on Christians but allow:
-
Domitian
-
Diocletian
-
Julian


Longtime Trump ally Roger Stone guilty of witness tampering, lying to Congress
by The Associated PressWASHINGTON -- Roger Stone, a longtime friend and ally of U.S. President Donald Trump, has been found guilty at his trial in federal court in Washington.
Stone was convicted Friday. He was charged in a seven-count indictment that alleged he lied to lawmakers about WikiLeaks, tampered with witnesses and obstructed a House intelligence committee probe.
His trial highlighted how Trump campaign associates were eager to gather information about emails hacked emails damaging to Hillary Clinton that were released by the anti-secrecy website WikiLeaks.
Prosecutors say Stone lied to Congress about his conversations about WikiLeaks with New York radio host Randy Credico and conservative writer and conspiracy theorist Jerome Corsi.
He's also accused of trying to intimidate Credico and threatening to take his dog.
Stone had denied the allegations and decried the case as politically motivated.


The core of mntyjs which handles everything around plugins
- Source:
Methods
(private, inner) addIdToElement(element)
Adds an id for internal use to the given element
Parameters:
| Name | Type | Description |
|---|---|---|
element |
HTMLElement | The element to add the id to |
- Source:
(private, inner) applyIdProperty(mountPoint) → {String}
Converts the mount point value to a valid data-* attribute
Parameters:
| Name | Type | Description |
|---|---|---|
mountPoint |
String |
- Source:
Returns:
- Type
- String
(private, inner) applyLoadFrom(loadFrom) → {String}
Prepares the plugin load path (strips trailing slashes)
Parameters:
| Name | Type | Description |
|---|---|---|
loadFrom |
String |
- Source:
Returns:
- Type
- String
(private, inner) applyMountPoint(mountPoint) → {String}
Converts the mount point value to a valid data-* attribute
Parameters:
| Name | Type | Description |
|---|---|---|
mountPoint |
String |
- Source:
Returns:
- Type
- String
(private, inner) determineElementPlugins({HTMLElement)) → {Array}
Determines the used plugins for the given element
and returns their names as array
Parameters:
| Name | Type | Description |
|---|---|---|
{HTMLElement) |
element The element to scan for plugins |
- Source:
Returns:
- Type
- Array
(private, inner) determinePluginOptions(element, pluginName) → {Object}
Returns the options of the plugin with given name for the given element
Parameters:
| Name | Type | Description |
|---|---|---|
element |
HTMLElement | The element to retrieve the plugin options from |
pluginName |
String | The name of the plugin to retrieve the options for |
- Source:
Returns:
- Type
- Object
(private, inner) determineUsedPlugins(elements) → {Array}
Determines the plugins which are used by the given elements
and returns an array with the appropriate plugin names
Parameters:
| Name | Type | Description |
|---|---|---|
elements |
Array | The elements to scan for plugins |
- Source:
Returns:
- Type
- Array
(private, inner) fetchPlugins(plugins, cb)
Fetches the needed plugins by requiring them by name
Parameters:
| Name | Type | Description |
|---|---|---|
plugins |
Array | An plugin name array of the plugins to fetch |
cb |
function | Function called after plugins are fetched |
- Source:
(private, inner) getElementId(element) → {Integer}
Returns the internal id of the given element
Parameters:
| Name | Type | Description |
|---|---|---|
element |
HTMLElement | The element to determine the id from |
- Source:
Returns:
- Type
- Integer
(inner) handleAttributesMutation(mutationRecord)
Handles an attribute mutation
Parameters:
| Name | Type | Description |
|---|---|---|
mutationRecord |
MutationRecord |
- Source:
(inner) handleChildListMutation(mutationRecord)
Handles a child list mutation
Parameters:
| Name | Type | Description |
|---|---|---|
mutationRecord |
MutationRecord |
- Source:
(inner) handleMutation(mutationRecord)
Handles a single mutation
Parameters:
| Name | Type | Description |
|---|---|---|
mutationRecord |
MutationRecord |
- Source:
(inner) handleMutations()
Handles any mutation of the DOM
- Source:
(private, inner) isPluginDisabled(pluginName) → {boolean}
Returns if the plugin with the given name is disabled
Parameters:
| Name | Type | Description |
|---|---|---|
pluginName |
String | Name of the plugin to test |
- Source:
Returns:
If plugin with given name is disabled or not
- Type
- boolean
(private, inner) lookupElements() → {jQuery}
Performs a lookup for elements in the DOM which use plugins
and returns them
- Source:
Returns:
- Type
- jQuery
(private, inner) mount()
Mounts the manager to given element
- Source:
(private, inner) mountAll(elements, plugins)
Mounts all plugin / options / element combinations
Parameters:
| Name | Type | Description |
|---|---|---|
elements |
Array | Array of elements which have plugins to mount |
plugins |
Array | Array of fetched plugin classes which have to be mounted |
- Source:
(private, inner) mountToElement(PluginClass, element, options) → {Plugin}
Creates an instances of the given plugin class for the given
element using the given options
Parameters:
| Name | Type | Description |
|---|---|---|
PluginClass |
Plugin | The Plugin to apply |
element |
HTMLElement | The element to mount the plugin to |
options |
Object | Configuration options |
- Source:
Returns:
The resulting plugin instance
- Type
- Plugin
(inner) process(root)
Processes mounting for given root element
Parameters:
| Name | Type | Description |
|---|---|---|
root |
HTMLElement |
- Source:
(private, inner) unmount()
Unmounts the manager from given element
- Source:
(private, inner) unmountPluginFromId(id, pluginName)
Unmounts the plugin with given name of element with
given internal id
Parameters:
| Name | Type | Description |
|---|---|---|
id |
Integer | The internal id of the element |
pluginName |
String | The plugin's name |
- Source:
(inner) unmountPluginsFromElement(element)
Parameters:
| Name | Type | Description |
|---|---|---|
element |
- Source:
(inner) unmountPluginsFromId(id)
Unmounts all plugin instances of element with
given internal id
Parameters:
| Name | Type | Description |
|---|---|---|
id |
Integer | The internal id of the element |
- Source:
(inner) unmountPluginsOfRoot(element)
Unmounts all plugins of given element
Parameters:
| Name | Type | Description |
|---|---|---|
element |
HTMLElement |
- Source:
(private, inner) updateBaseUrl(baseUrl)
Updates the base url for requirejs
Parameters:
| Name | Type | Description |
|---|---|---|
baseUrl |
String | The baseUrl the PluginManager should run from |
- Source:
(private, inner) updateLoggingEnabled(loggingEnabled)
Activates / deactivates logging
Parameters:
| Name | Type | Description |
|---|---|---|
loggingEnabled |
Boolean | Whether logging should be enabled or not |
- Source:
(private, inner) waitOnceFor(instance, event) → {Deferred}
Creates a deferred object for the given event on the given instance
Parameters:
| Name | Type | Description |
|---|---|---|
instance |
Plugin | The instance to wait for |
event |
String | The name of the event to wait for |
- Source:
Returns:
Deferred object which will be resolved when finished
- Type
- Deferred


Miranda grew up in the city of Seattle. She travelled South to Los Angeles for college and received her Bachelor’s degree in Biology. She moved to West Hollywood after graduation before eventually moving back to Seattle to study Front-end Web Development. She’s a strict follower of Ron Swanson’s Pyramid of Greatness.
Similar to Lewis and Clark, Rick moved out west to seek adventure and explore... and attend college. After graduating from Whitman with a BA in psychology, fate steered him towards software development. Rick is on the campaign trail with Swanson to ensure all Americans have full access to breakfast food, minimalist government, and a private hunting cabin.
Thom was born in Washington and has lived most of his life uncomfortable with speaking in the third person.
George was born in New York and has lived in California, Arizona, Louisiana, and Hong Kong. He attended undergrad at Loyola Marymount University in Los Angeles where he studied Finance and Economics before attending law school. He now resides in Seattle and studies Front-End Web Development. Drink of choice: Lagavulin.
Although he cannot grace us in person, Li'l Sebastian was a key member to the Swanson '16 campaign and is dearly missed. Li'l Sebastian, we miss you. But we know you are in heaven, looking down on us, doing your two favorite things: eating carrots and urinating freely.
Love,
The Team


Catch me if you can: Data Science Edition
The Federal Trade Commission’s online database of consumer complaints has compiled 13 million complaints from 2012 to 2016, with 3 million in 2016 alone. Of those, 42 percent were fraud related, and 13 percent were identity theft complaints.


Prime Solo Challenge 5
Modules, Routes, and AJAX in Node Servers
Indicate your minimum and maximum possible random balance:
Minimum
Maximum
Submit


|
ppm2tiff − create a TIFF file from PPM, PGM and PBM image files |
|
ppm2tiff [ options ] [ input.ppm ] output.tif |
|
ppm2tiff converts a file in the PPM, PGM and PBM image formats to TIFF. By default, the TIFF image is created with data samples packed (PlanarConfiguration=1), compressed with the Packbits algorithm (Compression=32773), and with each strip no more than 8 kilobytes. These characteristics can be overridden, or explicitly specified with the options described below If the PPM file contains greyscale data, then the PhotometricInterpretation tag is set to 1 (min-is-black), otherwise it is set to 2 (RGB). If no PPM file is specified on the command line, ppm2tiff will read from the standard input. |
|
−c |
Specify a compression scheme to use when writing image data: none for no compression, packbits for PackBits compression (will be used by default), lzw for Lempel-Ziv & Welch compression, jpeg for baseline JPEG compression, zip for Deflate compression, g3 for CCITT Group 3 (T.4) compression, and g4 for CCITT Group 4 (T.6) compression. |
|||
|
−r |
Write data with a specified number of rows per strip; by default the number of rows/strip is selected so that each strip is approximately 8 kilobytes. |
|||
|
−R |
Mark the resultant image to have the specified X and Y resolution (in dots/inch). |
|
tiffinfo(1), tiffcp(1), tiffmedian(1), libtiff(3) Libtiff library home page: http://www.remotesensing.org/libtiff/ |


iui-unloader.js is an extension for the iUI framework. It is currently EXPERIMENTAL
For info on how to use this extension see Instructions.
Sean Gilligan
The Unloader Extension is easy to install into your iUI powered web app. It requires no changes to existing iUI components. Start by linking the iui-unloader.js to your main index file just like you would iui.js.
All AJAX-loaded fragments will be unloadd when they receive the iUI 'unload' event when they are navigated away from (back button).
This should not be unloaded when you hit the back button


{% extends 'tools/base.html' %} {% block content %} Daisy Lanchester Username Visible to Public: dlan56 Your Posts Today at Work Walking home Saved Posts Yesterday this bad thing happened My boss said something innapropriate {% endblock %}


Take the virtual tour of Valley Transportation Authority
Pause the navigation any moment to know more about the intersting places around
When you are getting closer to a Stop, you can get off the tour and see the places nearby


--- layout: page title: Projects description: List of stuff i made. background: '/img/bg-post.jpg' ---
Websites I created using HTML, CSS, JS, Worpress, Laravel and .NET frameworks.
Some quick examples of games I made.
Some quick examples of banners I made.
Some quick examples of videos I made.


---
layout: page
title: "About me"
description: "Who I am and what I do."
header-img: "img/about-bg.jpg"
---
I am a PhD student at CentraleSupélec (Rennes) since October 2015. My PhD supervisors are : Pascal COTRET, Guillaume HIET, Vianney Lapôtre, Guy Gogniat and Christophe MOY. The thesis work is done in the frame of HardBlare project which is funded by CominLabs (50%) and Brittany region (50%). Before starting my PhD, I received an engineering degree in embedded systems from ENSEA and a M.Sc. in embedded autonomous systems from Université de Cergy-Pontoise (UCP).
I am interested in computer science, software/hardware security and embedded systems.
IETR, SCEE Research Group (Signal, Communications & Embedded Electronics)
UMR CNRS 6164, CentraleSupélec Rennes
Avenue de la Boulaie CS 47601
35576 Cesson-Sévigné Cedex – FRANCE
Email: muhammadabdul.wahab@domain_name
Replace domain_name by centralesupelec.fr.


Beginning with ACML 5.3.1, a new CPU affinity method is used by the OpenMP versions of the library. This method is used for DGEMM, SGEMM, and ZGEMM. The first time one of these routines is called by a program, each OpenMP thread will be bound to a specific CPU core. Core binding starts with core 0 and increments up to a maximum of 64 threads. In the Windows builds of ACML, this binding is implemented using SetThreadGroupAffinity. In the Linux builds, pthread_setaffinity_np is used. The environment variable OMP_NUM_THREADS is used to control the total number of OpenMP threads.


Wash the banana leaves under warm water.
Let the leaves rest on hot water in a large pot for one minute.
Remove the leaves from the water when they are tender and easy to bend without cracking.
Select one large banana leaf as the base of each tamale.
Place two more banana leaves over the based leaf, one above the other.
The leaves should be pointing in the opposite direction, like forming a cross.
Place a 1/2 of cup of dough in the center of the bed of leaves and spread it out.
Over the dough put one piece of beef,
a slice of carrot,
a chunk of potato,
a portion of pork,
another slice of carrot,
another chunk of potato,
one chicken drumette,
and one portion of egg.
Top it with another 1/2 of dough.
This does not have to be uniform!
There are different ways to close the tamale.
You can choose how to arrange the leaves
so the presentation of your final tamale will change.
To form a rounded tamale:
Pull up the leaves creating a spherical shape
and then tie the top with the kitchen string.
To form a Rectangular tamale:
Wrap the four ends of the leaves to form a packet
and then tie it around with the kitchen string.
Do not let any of the filling seep out.
If the leaves break just reinforce with extra banana leaves.
Use a large pot with steamer basket
so the tamales do not touch the water in the bottom.
Stack the tamales all the way to the top in the steamer pot.
Add water to cover almost all the tamales.
Cover the pot and bring it to a boil.
Lower the heat and simmer about 3 hours.
Keep checking the water level.
You probably will have to replenish the water during the cooking process.


Course Goal
Learn basics of HTML side of forms
Course Outline
- Forms Overview
- Our focus
- Get vs. Post
- Input types
- Building our own form
Pre-requisites
Understand HTML5 and its syntax
Forms Explained
Get vs. Post
<form action="forms_submit.php" method="get"> Form data here. </form>
GET
Get puts form data in the url as a query string
http://www.mysite.com/contact?name=Jon&age=30
Bookmarked, shared, remembered in browser history
Searches and queries
POST
Sends data in the body of the request
Not visible, sharable, or remembered by browser
Logins, form submissions, contact forms
Bigger chucks of info
Input types
<input type="text">
text
Single line of text info
textarea
Multiple lines of text
email input field; browser automatically validates
password
value is obscured with stars (does not encode the password)
Input types
checkbox
Select one or more options
radio
Select only one choice in group
Same name attribute to group radio buttons
submit
Button to submit form; calls action in form tag
© 2010 Trevor Davis Viget Labs


-
Disconnect
-
-
Connect to a server
Username (use your own)
Host
Connect
Server history
Password
Login
Pass sentence
-
Skip password
Setup password
PS: If you create a password and forget it there is no way to get back your pass sentence


ENVIRONMENT & HEALTH
By GROUP 5
JINGXI HUANG
YUJIE LI
YUE LIU
ince Under the Dome, a self-financed documentary film by Chai Jing, was first praised by the minister of environmental protection and later removed by Chinese Publicity department, haze issue has been annually mentioned when winter is coming. Starting from Beijing, the haze weather has swept across the whole country, including Hong Kong.
Transport emission was one of the biggest reasons for haze weather. In Hong Kong, the air quality is not satisfactory, in which roadside air quality is even worse. Hong Kong roadside air quality levels is still twice that of WHO’s upper limitation. Public transportation has become a major source of air pollution. The main pollutants include PM2.5, PM10, and NO2. Air pollution doesn't only affect the public health, but also causes direct and indirect economic losses.
What are the Key Pollutants?
Are you really know about your living place?
NO2
PM2.5
PM10
NOX
Nitrogen 0xide (NOX), may refer to a binary compound of oxygen and nitrogen. Nitrogen 0xide, especially NO2, is harmful to the nervous system of the human body. The excessive emission of Nitrogen 0xide can also cause acid rain. According to EPD HK, road traffic contributed to 17% of Nitrogen 0xide emission in 2015.
PM 10
PM10 refer to the particles with a diameter between 2.5 and 10 micrometers. PM10 can do harm to respiratory system and cardiovascular system, and cause asthma, lung cancer and cardiovascular disease. According to EPD HK, road traffic contributed to 9.02% of PM10 emission in 2015.
PM 2.5
PM2.5 refer to the particles with a diameter of 2.5 micrometers or less. PM2.5 has a larger superficial area than PM10, so it’s easier for PM2.5 to adsorb toxic substance. According to EPD HK, road traffic contributed to 10.49% of PM2.5 emission in 2015.
Death and Money Loss
Hedley Environmental Index, created by Dr.Hedley in 2008, is a new measurement method using economic loss and health burden to present the cost of air pollution.
The number of early deaths caused by air pollution has reached 2,196, causing economic damage $27.4 billion in 2016.
Through analysis towards the content of PM2.5, the number of early deaths and economic losses, it’s easy to see that during four years between 2013 and 2016, the three kind of values are positively correlated. It means research institutions can use the correlations to anticipate deaths and economic losses caused by air pollution using the data of PM2.5 in the future.
If the density of NO2 increases 10 micrograms, 134 people will die early, followed by 2 hundred million medical expenses and production losses. If the density of the particular matter increases 10 micrograms, local citizens’ death risk will grow by 1 per cent.
Calculate: What if the PM2.5 is⋯⋯
A Huge Difference
Hong Kong's standard of PM2.5 is 75 ug/m3 per 24 hours, while World Health Organization(WHO) sets it to 25. The pollutant PM2.5 is breathed directly into people's lungs, causing serious respiratory diseases. Compared to WHO, Hong Kong's standard is twice higher. As for PM10, HK standard is twice as much as that of WHO. Among all pollutants, only NO2 standard between Hong Kong and WHO is the same.
Air Quality Objectives, also known as AQOs, are Hong Kong Environment Protection plan of local air quality set out by The Air Pollution Control Ordinance (Cap.311). They are reviewed periodically, at least once every five years, in order to conserve and best use air for public interest.
However, they've become a serious health threat to the public. According to Friends of Earth, a local environmental organization, Hong Kong is placed in the last eight cities among 566 global cities ranked by the World Health Organization (WHO). In 2012, the Hong Kong government announced that it would finally update the AQO but they would not be implemented until 2014.
How Is The Air Quality Now?
Try to click the tags in the map and check the local pollutants. (Data from 28 April)
An "Effective" Little Improvement
Hong Kong government focus on only its “effective” improvement, saying: the government has been doing what was published on “A Clear Plan for Hong Kong” in 2013, and major pollutants from 2011 to 2015, PM10, PM2.5, and NO2 decreased respectively 19%, 24%, and 8%.
Changes of Roadside air quality for past 20 years
However, comparing to the pink line shown in above charts, in the past 20 years, Hong Kong real air quality hasn't reached the safe standard, overstepping WHO's standard as much as twice.
Environmental Protection Department has set 13 general stations and three roadside stations monitoring air quality. Figures collected from Mong Kok, Causeway Bay, and Central roadside stations showed that three pollutants average values are much higher than WHO’s standard. The condition has continued for 20 years.


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Davide works with accuracy, propriety and discretion, showing good human qualities and strong relational and organizational drive.
Davide's skills in Windows, Linux, VmWare and Security allowed him to show high accountability and professionalism that made him precious for the Public Prosecutor's Office in Rome.


| 455 | (A) | a plate of poleste golde pyghte on his breste |
| 459 | (C) | A plate of polysshed golde . was sette on his breeste |
| 456 | (D) | A plate of polisshid gold was put on his brest |
| 455 | (E) | a plate of polssched gold . was put on his brest |
| (Ex) | ---- no text ---- | |
| 472 | (L) | a plate of pulsched gold was piȝt on his breste |
| 435 | (P) | A plater of polist golde prest on his breste |
| 453 | (U) | A plate of polist gold / was put on his beest |
| (V) | ---- no text ---- |


Fortune Teller Quiz
Lucky Events
Winning a scratch ticket.
Found a four-leaf clover.
Found money.
Offered Free anything.
Got a job offer.
Got asked out by your crush.
Unlucky Events
Got hit by a car.
Broke a mirror.
Walked under a ladder.
Opened an umbrella inside.
Encounter with a black cat.
Stubbing your toe.
Submit Survey
Lady luck is with you.
Perhaps tomorrow the Gods will be on your side


Kings end Celts’ long win streak
Lakers subdue Hawks
The results:
LA Lakers 122, Atlanta 101; Orlando 125, Washington 121; New Orleans 108, Golden State 100; Denver 131, Memphis 114; Philadelphia 114, Cleveland 95; Sacramento 100, Boston 99
LOS ANGELES (AFP) - Bahamian guard Buddy Hield scored a season-high 35 points and the Sacramento Kings snapped the Boston Celtics’ 10-game NBA win streak with a 100-99 home triumph on Sunday.
Sacramento center Richaun Holmes sank two decisive free throws with 13 seconds remaining to give the Kings their fifth win in seven games after an 0-5 start.
 Definitely needed this win,” Hield said. “We’ve got to stay locked in and stay focused and be ready for every challenge every night.
“We know what we’re doing. We’re going to try to take it one game at a time and we’ll be fine.”
Boston fell to 10-2, having not lost since their season opener three-and-a-half weeks ago at Philadelphia.
The Celtics, led by Jaylen Brown’s 18 points, saw the end of their longest win streak since taking 16 in a row early in the 2017-18 campaign.
Hield’s three-pointer with 1:52 remaining gave the Kings a 96-95 lead.
Boston’s Jayson Tatum answered with a jumper but Hield, who went 14-of-24 from the floor, struck again with a fadeaway only for Boston to answer on a Daniel Theis dunk, giving the Celtics a 99-98 edge with 52 seconds remaining.
Holmes was fouled going for a rebound by Boston’s Marcus Smart and sank two free throws to put the Kings ahead by the final margin.
Smart missed a jumper for the win at the final buzzer and Sacramento had the victory.
Serbian swingman Bogdan Bogdanovic had 12 points and 10 assists for the Kings while Serbian forward Nemanja Bjelica added 12 points and 14 rebounds for Sacramento.
Boston guard Kemba Walker added 15 points and nine assists, Smart had nine assists, Brown grabbed eight rebounds, Tatum had 14 points and Theis contributed 14 points and 10 rebounds for the Celtics.
Elsewhere, LeBron James scored 33 points as the Los Angeles Lakers cruised to a 122-101 win over the struggling Atlanta Hawks.
The Lakers jumped out to a 25-point lead in the first quarter and never looked back.
Rajon Rondo scored 15 points and Anthony Davis added 14 as the Lakers posted their 11th win in their last 12 games since a season-opening loss to the Los Angeles Clippers.
Trae Young scored 31 points and Cam Reddish added 13 as the Hawks lost their third straight game.
Atlanta was blown out in Los Angeles for the second consecutive night after losing by 49 points to the Clippers.
Also, Denver’s Jamal Murray scored 39 points and Paul Millsap added 23 to lead the Nuggets over host Memphis 131-114 for their fifth win in six road contests. Jaren Jackson led the Grizzlies with 22 points.
Tobias Harris scored 27 points to lead six Philadelphia players in double figures as the 76ers rolled to a 114-95 triumph at Cleveland.


- turbowafflz.github.io (Web Developer/Game Developer) Taking over company, please wait...
Bredz (President/Game Developer)
(Web Developer/Game Developer)
NavyRed (Something Doer)
Ghast568 (Game Tester)
Some other people who are not listed here because they do nothing


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Climb Web Application
Bogota/ Colombia
Date: March 2020 - Present
Development and support for an web application call it CLIMB 2.0 orientend to, Animal Care, veterinary Animal Health, study management and research team and lab management. The architecture of this app:
- Frontend: Angular JS version 8, TypeScript, HTML5, CSS3, breeze.
- Backend: web api .NET, odata, ADO, entity framework
- Database: SQL, sql server, multitenant apps with multiple databases, sql sharding, factory patterns for migrations control.
- Work with git, github, azure devops.


Total unanimity between Cong-NCP, will meet Sena tomorrow: Prithviraj
The Congress and NCP leaders had held a marathon meeting on Wednesday to thrash out details of their alliance.
by PTINew Delhi: The Congress-NCP combine on Thursday said that the two parties have completed their discussion on all issues related to government formation in Maharashtra and will now hold talks with Shiv Sena in Mumbai to "finalise the architecture of alliance".
Former Maharashtra chief minister Prithviraj Chavan told reporters after a meeting of Congress and NCP leaders that there is a "complete unanimity" on all issues and when the alliance partners, which will include the Sena, announce their common minimum programme, then the power-sharing formula among the three parties will be disclosed.
The Congress and NCP leaders had held a marathon meeting on Wednesday to thrash out details of their alliance as they work to form a new government in the state in tie up with Sena.
They had expressed confidence of forming a stable government.
Catch the latest news, live coverage and in-depth analyses from India and World. Follow us on Facebook and Twitter.


Ruby Classes
Classes provide the framework for objects we use in our programs - they are integral to object-oriented programming and are a very powerful part of Ruby. Characters in a video game provide a great example. Rather than build separate objects for a ton of similiar characters, we can create a character type - or class - to base our characters off of. We can give this character class specific attributes (instance variables) or actions (methods) so they can interact with other players in the game. And when we need to create another character, we don't have to code an entirely new object. We can just copy, or initialize, a new character from the class by calling Class.new
To access or create a class, the syntax is simple:
end
Each class must have an initialize method. This method is used when calling the .new function to create a new object, or instance of the class. Any variables passed with the .new function are available for use in the initialize method and can be turned into instance variables to be used throughout the class. In the initialize method, you can also specificy criteria for a valid object by raising argument errors.
  @name = name
  @race = race
  @weapon = weapon
  @hometown = hometown
  raise ArgumentError.new("Your name is too long!") unless name.length < 50
end
Classes may have as many additional methods as you'd like, both regular and private. Private methods are not available outside the class, and cannot be called explicitly on an instance of an onject. They are available only within the class, to other instance methods. A classes methods are its actions - ways to manipulate its attributes (instance variables) so that the character can be dynamic and interact with other players.
  @hometown = new_hometown
end
def attack
  case @weapon
  when "sword"
    puts "You Slash Your Enemy!"
  when "magic"
    puts "You Cast a Bolt of Acid at Your Enemy!"
  when "bow"
    puts "You Fire a Flaming Arrow at Your Enemy!"
  end
end
With this code, a new character can now be created by calling Character.new, and passing through the attributes of the character. Once a character is created, we can now use the Character#change_hometown and Character#attack methods.
end
char1.change_hometown("Norwatch Castle")
char1.attack
And voila! You have your brand new character, ready to explore whatever magical world you've created!


{{text}}
Build your Fan Identity! Support your team by wearing the MLBand wherever you go
{{#each package}}
{{name}}
{{/each}}


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About Us
Duis autem vel eum iriure dolor in hendrerit in vulputate velit
esse molestie consequat, vel illum dolore eu feugiat nulla facilisis
Rakshan Foundation is a global network of 1.2 million neighbors, friends, leaders, and problem-solvers who see a world where people unite and take action to create lasting change – across the globe, in our communities, and in ourselves.
How We Work?
Our community partners guide us and our values drive us.
INTERCONNECTION
Our liberty is bound together. No person, community, or ecosystem exists in isolation, which means we need each other, and we need to learn from each other.
GROWTH MINDSET
We are constantly evaluating, listening, correcting, and innovating. We do not fail when we change course. Growth is an indicator of health and aliveness. We fail when we are unwilling to change.
INTEGRITY
We hold ourselves to a high standard. We are committed to being fair, objective, and proactively transparent in our decisions and behaviors. We honor our commitments and do the right thing.
LOCAL EXPERTISE
The most effective solutions will be crafted by a diverse and collaborative community, with those most affected by injustice at the head of the table.
ACTIVE HOPE
To build the world we need, we must choose to see a better way beyond the current reality, believe that a better way is possible, and commit to creating it.
GRIT
We do not see challenges and setbacks as barriers to success, but as invitations to think creatively and push forward with courage and resolution.
What makes us different?
Projects you can trust.
We fully vet every nonprofit in our community. We’ve even conducted site visits for most organizations—even those in the remote areas!
We’re not-for-profit.
Unlike some crowdfunding and technology platforms, our mission is to help nonprofits and donors have more impact, not to make a profit.
We match donations.
We frequently offer campaigns with matching money and bonus prizes to help motivate donors.
Donor satisfaction is guaranteed.
If you don’t like how your donation has been put to work, we’ll let you re-allocate the money to a new project of your choice.
Training and support.
We offer tools, training, and one-on-one support to help nonprofits get the funding and resources they need.
Direct connections.
Rakshan donors know exactly who and what they are funding. They can send and receive messages and get regular updates.
Follow us on Social Media
इनमें से कौन सी अभिनेत्री इस साल आपको लगी सबसे तेज़? रिप्लाई सेक्शन में बताएं और SMS भेजकर अपनी पसंदीदा हीरोइन को जिताएं।#YearEnder2020 #Promo pic.twitter.com/G2swCdyN8o
— AajTak (@aajtak) December 21, 2020
IT’S MORE THAN JUST A JOB
OUR TEAM
We’re curious, passionate, and committed to helping nonprofits learn and grow. Oh, and some of us are stage actors and worm farmers on the side.
Rishabh Arya
Founder
We love that guy
Rishabh Arya
Architect & Engineer
We love that guy
Rishabh Arya
Architect & Engineer
We love that guy
Rishabh Arya
Architect & Engineer
We love that guy
OUR SPONSORS
Meet the companies that power change around the world with Rakshan Foundation


Prevents horizontal scrolling by forcing columns to occupy 100% of the container's width. If the container is too small to fit all columns, a horizontal scrollbar will appear regardless, but users will not be allowed to re-size columns even with options.resizableColumns
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{autoColumnWidth: true}


“There’s an entrepreneur right now, scared to death, making excuses, saying, ‘It’s not the right time just yet.’ There’s no such thing as a good time.
I started an apparel-manufacturing business in the tech-boom years.
And the only way to do great work is to love what you do.”
“Embrace what you don’t know, especially in the beginning, because what you don’t know can become your greatest asset.
It ensures that you will absolutely be doing things different from everybody else.”
“It’s not about having a specific set time; both personal and professional lives are 24/7.
It’s simply, more about making the right allocation to each one and recognizing that it’s going to be different every single day.”
“The question I ask myself like almost every day is, ‘Am I doing the most important thing I could be doing?’…
Unless I feel like I’m working on the most important problem that I can help with, then I’m not going to feel good about how I’m spending my time.
And that’s what this company is.”
“I always did something that I was a little not ready to do. I think that, that is how you grow.
When there’s a moment of ‘Wow, I’m not so sure that I can do this, and you push through those moments, it’s then that you have a breakthrough.
Sometimes that’s a sign that something really great is about to happen.
You’re about to grow and learn a lot more about yourself.”


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Want to get in touch with me?
My contact details
| Skype | pasha.shaik3 |
|<EMAIL_ADDRESS>| |
| https://in.linkedin.com/pub/pasha-shaik/4a/241/b72 | |
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skype: pasha.shaik3


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Hi there, I'm a biologist turned full stack web/app developer with experience in RoR, JS, HTML5, CSS/SASS, and Agile development. I'm mechanically inclined, play all sorts of sports, and have many favorite TV shows (HIMYM, The Office, Modern Family just to name a few). I am Ricky Do


Bagel the Cat: since i learned to code mi vida ha mejorado
Posted by: Bagel J Cat :)
Yowling nonstop the whole night stand with legs in litter box, but poop outside groom forever, stretch tongue and leave it slightly out, blep but eat the rubberband. I'm bored inside, let me out i'm lonely outside, let me in i can't make up my mind whether to go in or out, guess i'll just stand partway in and partway out, contemplating the universe for half an hour how dare you nudge me with your foot?!?! leap into the air in greatest offense! flop over, for kitty power or chase after silly colored fish toys around the house yet where is my slave? I'm getting hungry for eat the rubberband so you have cat to be kitten me right meow.
Attack the dog then pretend like nothing happened if human is on laptop sit on the keyboard but jump on human and sleep on her all night long be long in the bed.
Please stop looking at your phone and pet me.
-Thought for the Day. :)


| Resizes an image to a new size using pixel values. |
| image = file handle for previously loaded image width# = new width in pixels height# = new height in pixels |
Command Description:
| Similar to ScaleImage, but uses pixel values instead of percentages. Use this command to resize an image previously loaded with LoadImage or LoadAnimImage. This is NOT intended for REAL TIME scaling of images! Precalculate your images before running your program, or you will likely see massively slow renderings of graphics. |
Example:
| ; ResizeImage example ; Set Graphics Mode Graphics 800,600,16 ; Randomize the random seed SeedRnd MilliSecs() ; Load an image to tile (your location might vary) gfxBall=LoadImage("C:\Program Files\Blitz Basic\samples\ball.bmp") ; Size it randomly from 300 to -300 both x and y ResizeImage gfxBall,Rnd(-300,300),Rnd(-300,300) ; Wait for ESC to hit While Not KeyHit(1) DrawImage gfxball,Rnd(800),Rnd(600) VWait Wend |


Hi my name is Kaitlyn and I am an aspiring web developer. I'm currently working on my degree in IT and Web Development from UMass Lowell and also hold a BS in Biology from the same school. Some of the languages I have studied so far are: HTML, CSS, Javascript, Java, and Python. Currently, I am studying React and hope to learn Django after.
One of my favorite projects that I worked on for a class was my travel site about the Netherlands. I loved that I got to showcase a place I really loved through creating a website with all my coding knowledge. Being able to create something from the bottom up and watching it grow into something real is a great feeling.
View ResumeHave you ever thought about traveling abroad to Europe? Why not check out the Netherlands, the small country with a lot to offer. This website was made using Javascript, jQuery, Bootstrap, HTML, and CSS.
This was a fun little app created in React.js through the Zero to Mastery course on Udemy. It has a search feature that filters the robots, uses an API to fetch the robots, and is fully responsive!
This was my first real project working with API's in React. The Pokemon API on the web was used to gather the information from to get the Pokemon name, height, weight, attacks, etc, as well as their sprite picture. It loops through all the Pokemon and has prev and next buttons to cycle through.
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